Background
Diabetic kidney disease (DKD) has become the primary cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). In China, the prevalence of DKD in patients with type 2 diabetic mellitus (T2DM) was 21.8%, and diabetes was responsible for roughly 14.7% of treated ESRD1,2. Abnormal glucose metabolism can cause renal endothelial dysfunction and podocyte dysfunction, resulting in a prolonged rise in albuminuria and a decrease in the estimated glomerular filtration rate (eGFR), optimize glucose control may reduce the risk or slow the progression of CKD3.
For DKD patients, thorough monitoring of blood glucose fluctuations is critical. Glycated hemoglobin (Hemoglobin A1c, HbA1c) and glycated albumin (GA) are now accept-ed markers of glycemic management4. In the last 2–3 months, HbA1c has been regarded as the gold standard of blood glucose management. GA is a glycated protein of the amadori type that shows glycemic control during the previous 14–20 days. According to several studies, GA is a better measure of glycemic control than HbA1c in diabetics and dialysis patients5, 6–7.
HbA1c and GA are currently not effective glucose monitoring markers during DKD for a variety of reasons8.The detection of HbA1c is influenced by red blood cell life and membrane permeability, such as hemoglobinopathy, hemolysis, anemia (e.g., iron deficient anemia, renal anemia), and other factors that alter hemoglobin glycosylation9. Different circumstances in CKD might cause HbA1c to be overestimated or underestimated. Kidney Disease: Improving Global Outcomes (KDIGO) recommends that HbA1c readings in individuals with low eGFR should be interpreted with care10. The limitations of HbA1c are not commonly understood. It has been proposed that GA be employed to compensate for the absence of HbA1c in this scenario11. However, several investigations have found that the association between GA and blood glucose is not significant in ESRD patients12. This discrepancy may be induced by albumin turnover and leakage, indicating that albumin turnover leakage affects GA.
There is little question that a number of variables influence albumin turnover leakage. The quantity of protein lost in the urine is more easily available, and the protein content in the urine of a healthy person is very low, and it hardly affects the cyclical metabolism of the protein. However, for patients with large amounts of protein in urine, the protein lost from urine should not be overlooked. It has been demonstrated that serum GA in dialysis patients is affected by protein loss in urine and hemodialysis fluid13. It has also been proven that the shortened half-life of protein in nephrotic syndrome (NS) and increased loss of urine lead to a decrease in the body’s protein mass, which may be one of the rea-sons for low GA measurements14,15.
When analyzing a patient’s GA level, several potential influences on GA readings must be considered, however there is presently insufficient evidence to systematically compensate for GA values. Researchers are investigating several GA correction strategies to enhance the accuracy of glucose monitoring measures in the advanced stages of DKD. Kei Fukami et al.16 came to the conclusion that serum albumin-adjusted GA is a superior predictor of glycemic control in ESRD diabetic patients who are not on hemodialysis, and they developed a regression equation for GA against serum albumin in 49 ESRD diabetic patients who are not on hemodialysis. Fei Y et al.17 corrected glycated albumin based on normal adult albumin turnover metabolism, and they affirmed the clinical value of corrected GA to assess blood glucose levels in DKD patients combined with macroalbuminuria.
In this study, we attempted to adjust GA in terms of albumin turnover, as well as evaluate the influence of proteinuria on the clinical assessment of GA and the adjustment for GA.
Methods
Study populations
This study included 195 patients aged 18–80 years with type 2 diabetic kidney disease (T2DKD) who attended the nephrology department of Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University between October 2016 and December 2021.
Individuals with any of the following were excluded: patients with severe primary disease; participants with severe infections; patients with acute complications of diabetes; patients on renal replacement therapy; individuals with protein-losing enteropathy; individuals with blood transfusions within 12 weeks; individuals with albumin transfusions within 12 weeks; patients with adjustment of glucose management regimen within 12 weeks; pregnant women; participants with cancers.
The study protocol was considered and approved by the Medical Ethics Committee of Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University. All methods were performed in accordance with the relevant guidelines and regulations of Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University.
Diagnostic criteria
The 1999 World Health Organization diagnostic criteria were used to determine the diagnosis of T2DM18. DKD was defined as diabetes with UACR of ≥ 30 mg/g and/or eGFR of < 60 ml/min/1.73 m2 in the absence of signs or symptoms of other primary causes of kidney damage according to the KDIGO guidelines19 and Chinese clinical practice guidelines20. The eGFR was calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation21. Referring to the 2012 KDIGO guidelines19, the staging is based on glomerular filtration rate and urinary albumin level. Ophthalmologists used a standardized fundoscopic examination to identify diabetic retinopathy in accordance with the International Clinical Diabetic Retinopathy Disease Severity Scale22.
Hemoglobin (Hgb) 120 g/L in males or 110 g/L in females was the threshold for non-anemia. Serum albumin (ALB) levels below 35 g/L were considered hypoproteinemic. The HbA1c compliance levels were established using the Chinese recommendations23. The optimal HbA1c values for G1-G3a are 7.5% and below, while for G3b-G5 they are 8.5% and below.
Measurements
Patients’ blood samples were taken following an overnight fast. All tests were done at the Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University’s Department of Laboratory Medicine. The Lucica GA-L enzymatic method was used to quantify serum GA using a Backman AU5821 automated biochemistry analyzer. HbA1c was determined using a Tosoh automated glycated hemoglobin analyzer G11 (HLC-723G11) and high performance liquid chromatography. The GA is often evaluated using the Lucica GA-L enzymatic approach, in which the GA is digested to amino acids by albumin-specific proteases, followed by its oxidation to hydrogen peroxide by ketamine oxidase. The proportion of quantitative GA relative to albumin was used to compute measured GA (mGA) levels in order to account for variances not caused by individual albumin concentrations24.
We recorded the following information in the HIS system of Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University: age, gender, weight, history of smoking, history of alcohol consumption, history of hypertension and diabetes mellitus. This information was collected through face-to-face interviews and entered into the HIS system.
Research methods
To evaluate the accuracy of mGA under various albuminuria circumstances, patients were divided into the following two groups according to the 24-h urine protein (24hUP) level: group 1 (UP1), 24hUP ≤ 3500 mg/24 h and group 2 (UP2), 24hUP > 3500 mg/24 h. To evaluate the accuracy of the index at different serum albumin (ALB) levels, patients were divided into the following two groups according to the ALB level: group 1 (SA1), ALB ≥ 35 g/L and group 2 (SA2), AL < 35 g/L. When considering albumin distribution and metabolism, individuals with macroalbuminuria have a plasma to interstitial fluid albumin ratio of around 2:314. We referred to and revised the adjustment formula for GA originally proposed by Fei Y et al.17, with the aim of further minimizing the impact of protein leakage on mGA in patients with DKD.Specifically, we considered that the normal metabolic lifespan of albumin is shortened in DKD patients with significant albuminuria due to increased urinary albumin loss. This accelerated turnover reduces the time available for glycation, leading to an underestimation of glycemic control when assessed by mGA. To quantify this effect, we calculated the additional metabolic burden imposed by urinary protein loss as the ratio of UP to the total body albumin pool, which includes albumin distributed in both plasma and interstitial fluid compartments. The adjusted metabolic days are therefore equal to the normal metabolic days plus an additional term reflecting the impact of protein loss through urine.The ratio of the normal metabolic days to the adjusted metabolic days represents the degree to which mGA underestimates true glycemic exposure. This ratio is then applied as a correction factor: the adjGA is obtained by multiplying mGA by the ratio of normal metabolic days to adjusted metabolic days. In this way, the adjGA corrects for the accelerated albumin turnover associated with proteinuria and offers a more accurate reflection of glycemic control, particularly in DKD patients with macroalbuminuria.Based on this principle, we established two correction formulas. The outcome of Fei Y et al.’s original method is referred to as cGA, which is calculated as follows:
cGA = mGA×[1+(K×UP) ÷ (V×SA)]
In our revised adjustment formula, we incorporated further considerations regarding the distribution and metabolism of albumin between plasma and interstitial fluid compartments, resulting in the following equation for adjGA:
adjGA = mGA×[1+(8×K×UP) ÷ (11×V×SA)]
where mGA is the actual measurement of GA; K is the standard metabolic days of albumin, using the normal adult albumin metabolic days, i.e. 15 d ; V is the plasma volume (L), the normal human plasma volume is about 5% of body weight, i.e. V = m×5% (L), m is body weight (kg) ; UP is 24-h urine protein, unit is g/24 h ; SA is Serum albumin, unit is g/L.
We referred to the mean blood glucose (MBG) model for patients with CKD established by Liu XX25 : MBG (mmol/L) = 4.539 + 0.95×HbA1c (%)-0.016×Hgb(g/L) (corrected R2 = 0.829, P < 0.001).
Statistics analysis
The mean and standard deviation (SD) or median (interquartile range [IQR]: Q1-Q3) of the patient’s demographic and clinical data were used, as applicable. Counts (or percentages) were used to report categorical variables. Depending on the distribution of the data, the measures were assessed using the Student’s t-test or Mann-Whitney U test, and categorical variables were subjected to the chi-square test. Correlations between GA (mGA, adjGA, and cGA) and different variables were determined by computing Spearman correlation coefficients (rS) and multivariate linear regression analyses. We described the curves of GA-L and urinary protein levels according to the grouping of average blood glucose, found the range of turning points of the three groups of curves, and conducted segmentation according to this cut-off point, respectively analyzed the correlation between the data before and after, and described the correlation curves. The inclusion of multivariate linear regression was based on the significance (P < 0.05) or clinical significance in univariate analysis. P-values < 0.05 with two-tailed were considered as a statistically significant level. The statistical analysis tests were conducted using SPSS 26.0 statistical software (IBM Corp, Armonk, NY).
Results
Population characteristics
The average age of the 195 participants was 59.97 years, with 139 (71.3%) being male. For all subjects, the median eGFR was 41.22 mL/min/1.73 m2, the median UACR was 3051.57 mg/g, and the median 24hUP was 3189 mg/24 h. In UP1, mGA was 19.75% and adjGA was 22.32%, with a median HbA1c of 7.5%, an eGFR of 45.98 mL/min/1.73 m2, a mean ALB of 36.41 g/L and a mean Hgb of 119.12 g/L; and in UP2, mGA was 13.20% and adjGA was 22.45%, with a median HbA1c of 6.6%, an eGFR of 34.97 mL/min/1.73 m2, a mean ALB of 28.60 g/L, and a mean Hgb of 109.68 g/L. The mGA was substantially different between UP subgroups (P < 0.001), but not the adjGA, as shown in Fig. 1. In addition, the UP2 group had substantially lower age, diseases duration, mGA, eGFR, and ALB than the UP1 group (P < 0.001). The data are shown in Table 1.
Table 1. Population characteristics.
Variables | ALL(n = 195) | UP1(n = 108) | UP2 (n = 87) | t/ Z/χ2 | P-value |
|---|---|---|---|---|---|
Age (years) | 59.97 ± 11.53 | 63.16 ± 11.12 | 56.01 ± 10.83 | 4.512a | < 0.001** |
Male, n (%) | 139 (71.3%) | 72 (66.7%) | 67 (77.0%) | 2.519c | 0.112 |
DM duration (months) | 180 (120–240) | 216 (144–264) | 150 (105–240) | -3.737b | < 0.001** |
Smoker, n (%) | 90 (46.2%) | 43 (39.8%) | 47 (54.0%) | 3.914c | 0.048 |
Drinking, n (%) | 57 (29.22%) | 31 (28.7%) | 26 (29.9%) | 0.033c | 0.857 |
DR, n (%) | 152 (77.9%) | 82 (75.9%) | 70 (80.5%) | 0.532c | 0.466 |
Hypertension (%) | 178 (91.3%) | 97 (89.8%) | 81 (93.1%) | 1.209c | 0.272 |
Body weight (kg) | 75.00 (66.00–85.00) | 72.00 (62.25-80.00) | 77.00 (66.60–88.00) | -2.223b | 0.026* |
mGA (%) | 16.70 (12.90–21.10) | 19.75 (15.45–23.60) | 13.20 (10.90–16.60) | -7.923b | < 0.001** |
adjGA (%) | 22.43 (18.45–26.27) | 22.32 (18.20-26.43) | 22.45 (18.67–25.52) | -0.388b | 0.698 |
cGA (%) | 24.15 (20.26–28.47) | 23.23 (19.05–27.47) | 25.86 (21.09–28.85) | -1.909b | 0.056 |
HbA1c (%) | 7.20 (6.30–8.20) | 7.50 (6.60–8.57) | 6.60 (5.90–8.10) | -2.870b | 0.004* |
FBG (mmol/L) | 6.93 (5.56-9.00) | 7.29 (6.08–9.59) | 6.40 (5.29–8.29) | -2.836b | 0.005* |
MBG (mmol/L) | 9.44 (8.73–10.50) | 9.71 (9.02–10.67) | 9.22 (8.62–10.47) | -2.315b | 0.021* |
SCr (umol/L) | 155.00 (96.00-240.40) | 112.80 (82.87–205.70) | 179.00 (140.40-271.80) | -4.994b | < 0.001** |
eGFR (mL/min/1.73 m2) | 41.22 (22.26–65.98) | 45.98 (24.34–79.18) | 34.97 (18.01–43.59) | -4.315b | < 0.001** |
UACR (mg/g) | 3051.57 (1069.42-5156.28) | 1502.35 (551.68-2886.46) | 4983.64 (3377.61-6488.05) | -8.435b | < 0.001** |
24hUP (mg/24 h) | 3189.00 (1379.30-5453.20) | 1539.40 (637.60-2324.37) | 5752.80 (4554.30–7155.00) | -11.992b | < 0.001** |
ALB (g/L) | 32.94 ± 7.46 | 36.41 ± 6.30 | 28.60 ± 6.42 | 8.530a | < 0.001** |
Hgb (g/L) | 114.94 ± 23.06 | 119.12 ± 23.22 | 109.68 ± 21.88 | 2.883a | 0.004* |
CRP (mg/L) | 2.60 (1.40–4.90) | 2.50 (1.38–4.55) | 2.70 (1.45–4.95) | -0.283b | 0.777 |
SUA (umol/L) | 412.00 (350.10-478.10) | 395.55 (342.77-476.75) | 424.00 (367.40–482.00) | -1.274b | 0.203 |
LDL-C (mmol/L) | 3.10 (2.24–3.94) | 2.85 (2.05–3.59) | 3.62 (2.49–4.42) | -3.319b | 0.001* |
TG (mmol/L) | 1.73 (1.24–2.45) | 1.59 (1.16–2.19) | 1.96 (1.40–2.85) | -2.768b | 0.006* |
DM, diabetes mellitus; DR, diabetic retinopathy; DKD, diabetic kidney disease; mGA, measured glycated albumin; adjGA, adjusted glycated albumin; cGA, corrected glycated albumin; HbA1c, Hemoglobin A1c; FBG, fasting blood glucose; MBG, mean blood glucose; SCr, Serum creatinine; eGFR, estimated glomerular filtration rate; UACR, urinary al-bumin/creatinine ratio; 24hUP, 24-h urine protein; ALB, albumin; Hgb, Hemoglobin; CRP, C-reactive protein; SUA, serum uric acid; LDL-C, low-density lipoprotein cholesterol; TG, tri-glycerides.
Data are presented as the mean ± standard deviation or median (IQR: Q1–Q3) for continuous variables and percentage for categorical variables. * To UP1, a two-sided P-value < 0.05 was considered statistically significant, * * To UP1, a two-sided P-value < 0.001.
a Student’s t-test analysis.
b Mann-Whitney U analysis,
c chi-square test analysis.
Fig. 1 [Images not available. See PDF.]
GA values in UP subgroups. The mGA was substantially different between UP subgroups (P < 0.001), but not the adjGA or cGA.
Analysis of factors related to mGA, AdjGA and cGA
As demonstrated in Table 2, simple correlation analysis revealed a strong negative association between mGA and 24hUP and UACR (P < 0.05), and a positive correlation with the duration of diabetes, HbA1c, FBG and ALB (P < 0.001), irrespective of age, gender, and drinking (P > 0.05), as shown in Table 2. Multiple regression analysis revealed that one of the independent influencing factors for mGA was 24hUP (P < 0.001). The data are shown in Table 3.
According to Table 2, simple correlation analysis revealed a strong positive association between adjGA and DM duration, body weight, FBG, MBG, and HbA1c (P < 0.05), in-dependent of 24hUP, and ALB (P > 0.05). Multiple regression analysis confirmed that body weight and HbA1c were independent influencing factors of adjGA (P < 0.001), as shown in Table 4.
Simple correlation analysis showed positive correlations between cGA and MBG, HbA1c, HbA1c, and SCr (P < 0.05), and a negative correlation with eGFR (P < 0.05), inde-pendent of DM duration and ALB (P > 0.05), as shown in Table 2. Multiple regression showed that 24hUP and SCr was two of the influencing factors of cGA (P < 0.05), data shown in Table 5.
Table 2. Simple correlation analysis of mGA, AdjGA and cGA.
Clinical related indicators | mGA (n = 195) | adjGA (n = 195) | cGA (n = 195) | |||
|---|---|---|---|---|---|---|
rS | P-value | rS | P-value | rS | P-value | |
Ages (years) | 0.173 | 0.016 | 0.013 | 0.856 | -0.027 | 0.711 |
Gender | 0.060 | 0.404 | 0.076 | 0.294 | 0.071 | 0.321 |
DM duration (months) | 0.276 | < 0.001 | 0.149 | 0.039 | 0.095 | 0.189 |
Smoker | -0.165 | 0.021 | -0.131 | 0.068 | -0.099 | 0.170 |
Drinking | 0.002 | 0.997 | -0.019 | 0.789 | -0.026 | 0.715 |
Diabetic retinopathy | -0.007 | 0.923 | 0.019 | 0.794 | 0.043 | 0.560 |
Hypertension | -0.157 | 0.029 | -0.107 | 0.136 | -0.077 | 0.286 |
Body weight (kg) | -0.130 | 0.071 | -0.178 | 0.013 | -0.186 | 0.009 |
HbA1c (%) | 0.489 | < 0.001 | 0.442 | < 0.001 | 0.385 | < 0.001 |
FBG (mmol/L) | 0.326 | < 0.001 | 0.164 | 0.022 | 0.111 | 0.124 |
MBG (mmol/L) | 0.486 | < 0.001 | 0.508 | < 0.001 | 0.465 | < 0.001 |
SCr (umol/L) | -0.236 | 0.001 | 0.103 | 0.152 | 0.194 | 0.006 |
eGFR (mL/min/1.73 m2) | 0.233 | 0.001 | -0.097 | 0.176 | -0.186 | 0.009 |
UACR (mg/g) | -0.535 | < 0.001 | -0.037 | 0.615 | 0.113 | 0.124 |
24hUP (mg/24 h) | -0.631 | < 0.001 | -0.004 | 0.953 | 0.185 | 0.009 |
ALB(g/L) | 0.514 | < 0.001 | 0.029 | 0.687 | -0.115 | 0.108 |
Hgb(g/L) | 0.198 | 0.006 | -0.060 | 0.409 | -0.128 | 0.075 |
CRP (mg/L) | -0.050 | 0.491 | -0.010 | 0.895 | -0.008 | 0.917 |
SUA (umol/L) | 0.004 | 0.955 | 0.093 | 0.196 | 0.103 | 0.152 |
LDL-C (mmol/L) | -0.205 | 0.004 | -0.025 | 0.731 | 0.033 | 0.650 |
TG (mmol/L) | -0.077 | 0.288 | 0.027 | 0.706 | 0.059 | 0.416 |
DM, diabetes mellitus; DR, diabetic retinopathy; DKD, diabetic kidney disease; mGA, measured glycated albumin; adjGA, adjusted glycated albumin; cGA, corrected glycated albumin; HbA1c, Hemoglobin A1c; FBG, fasting blood glucose; MBG, mean blood glucose; SCr, Serum creatinine; eGFR, estimated glomerular filtration rate; UACR, urinary albumin/creatinine ratio; 24hUP, 24-h urine protein; ALB, albumin; Hgb, Hemoglobin; CRP, C-reactive protein; SUA, serum uric acid; LDL-C, low-density lipoprotein cholesterol; TG, triglycerides.
A two-sided P-value < 0.05 was considered statistically significant.
Table 3. Multiple regression analysis affecting mGA.
Independent variable | Non-standardized β | Standard error | Beta | t | P-value |
|---|---|---|---|---|---|
Constant | 4.908 | 3.304 | 1.486 | 0.139 | |
Ages (years) | -0.007 | 0.027 | -0.014 | -0.273 | 0.785 |
DM duration(months) | 0.006 | 0.003 | 0.110 | 2.103 | 0.037 |
Smoker | -0.620 | 0.575 | -0.052 | -1.079 | 0.282 |
Hypertension | -1.572 | 1.065 | -0.074 | -1.475 | 0.142 |
HbA1c (%) | 1.713 | 0.208 | 0.491 | 8.249 | < 0.001 |
FBG (mmol/L) | 0.232 | 0.125 | 0.114 | 1.857 | 0.065 |
SCr (umol/L) | 0.005 | 0.002 | 0.153 | 2.471 | 0.014 |
eGFR (mL/min/1.73 m2) | 0.001 | 0.014 | 0.005 | 0.067 | 0.947 |
UACR (mg/g) | -0.002 | 0.001 | -0.112 | -1.687 | 0.093 |
24hUP (mg/24 h) | -0.001 | 0.000 | -0.319 | -4.705 | < 0.001 |
ALB (g/L) | 0.188 | 0.052 | 0.243 | 3.653 | < 0.001 |
Hgb (g/L) | -0.046 | 0.016 | -0.179 | -2.850 | 0.005 |
LDL-C(mmol/L) | -0.001 | 0.016 | -0.003 | -0.070 | 0.945 |
DM, Diabetes mellitus; HbA1c, Hemoglobin A1c; FBG, fasting blood glucose; SCr, Serum creatinine; eGFR, Estimated Glomerular Filtration Rate; UACR, Urinary albumin/creatinine ratio; 24hUP, 24-h urine protein; ALB, albumin; Hgb, Hemoglobin; LDL-C, low-density lipoprotein cholesterol.
A two-sided P-value < 0.05 was considered statistically significant.
Table 4. Multiple regression analysis affecting AdjGA.
Independent variable | Non-standardized β | Standard error | Beta | t | P-value |
|---|---|---|---|---|---|
Constant | 15.509 | 2.626 | 5.905 | < 0.001 | |
DM duration(months) | 0.005 | 0.004 | 0.081 | 1.341 | 0.182 |
Body weight(kg) | -0.099 | 0.026 | -0.230 | -3.828 | < 0.001 |
HbA1c (%) | 1.760 | 0.255 | 0.491 | 6.912 | < 0.001 |
FBG (mmol/L) | 0.076 | 0.145 | 0.037 | 0.521 | 0.603 |
DM, Diabetes mellitus; HbA1c, Hemoglobin A1c; FBG, fasting blood glucose.
A two-sided P-value < 0.05 was considered statistically significant.
Table 5. Multiple regression analysis affecting cGA.
Independent variable | Non-standardized β | Standard error | Beta | t | P-value |
|---|---|---|---|---|---|
Constant | 12.482 | 2.991 | 4.173 | < 0.001 | |
Body weight(kg) | -0.127 | 0.028 | -0.262 | -4.502 | < 0.001 |
HbA1c (%) | 2.447 | 0.249 | 0.606 | 9.822 | < 0.001 |
SCr (umol/L) | 0.009 | 0.003 | 0.230 | 3.145 | 0.002 |
eGFR (mL/min/1.73 m2) | -0.011 | 0.016 | -0.056 | -0.704 | 0.482 |
24hUP (mg/24 h) | 0.001 | 0.000 | 0.255 | 4.082 | < 0.001 |
HbA1c, Hemoglobin A1c; SCr, Serum creatinine; eGFR, Estimated Glomerular Filtration Rate; 24hUP, 24-h urine protein.
A two-sided P-value < 0.05 was considered statistically significant.
Correlation of GA with blood glucose in the albuminuria and eGFR subgroups
In UP1, mGA had a positive association with MBG (rS =0.662, P < 0.001), but in UP2, there was no such link. In addition, adjGA correlated positively with MBG in both albu-minuria categories (In UP1, rS =0.672, P < 0.001; In UP2, rS =0.299, P < 0.05). The mGA and adjGA showed similar correlations with FBG in the albuminuria subgroups (Fig. 2). The data are shown in Table 6.
Regarding HbA1c, its associations with GA metrics varied. HbA1c was positively correlated with mGA, adjGA, and cGA in both UP1 and UP2 groups. In UP1, HbA1c showed strong correlations with mGA (rS = 0.701, P < 0.001) and adjGA (rS = 0.695, P < 0.001), and a moderate correlation with cGA (rS = 0.545, P < 0.001). In UP2, HbA1c remained significantly correlated with mGA (rS = 0.356, P < 0.001) and adjGA (rS = 0.348, P < 0.001), but the association with cGA was weak (rS = 0.112, P = 0.124).
Table 6. Correlation coefficients between GA metrics and glycemic parameters in different albuminuria groups.
All(n = 195) | UP1(n = 108) | UP2(n = 87) | |||||
|---|---|---|---|---|---|---|---|
rS | P-value | rS | P-value | rS | P-value | ||
MBG | mGA | 0.486 | < 0.001 | 0.662 | < 0.001 | 0.197 | 0.068 |
adjGA | 0.508 | < 0.001 | 0.672 | < 0.001 | 0.299 | 0.005 | |
cGA | 0.465 | < 0.001 | 0.663 | < 0.001 | 0.303 | 0.005 | |
FBG | mGA | 0.489 | < 0.001 | 0.351 | < 0.001 | 0.121 | 0.266 |
adjGA | 0.442 | 0.022 | 0.296 | 0.002 | 0.014 | 0.895 | |
cGA | 0.385 | 0.124 | 0.27 | 0.005 | -0.021 | 0.85 | |
HbA1c | mGA | 0.489 | < 0.001 | 0.351 | < 0.001 | 0.121 | 0.266 |
adjGA | 0.442 | < 0.001 | 0.296 | 0.002 | 0.014 | 0.895 | |
cGA | 0.385 | < 0.001 | 0.27 | 0.005 | -0.021 | 0.85 | |
mGA, measured glycated albumin; adjGA, adjusted glycated albumin; cGA, corrected glycated albumin; MBG, mean blood glucose.
A two-sided P-value < 0.05 was considered statistically significant.
Fig. 2 [Images not available. See PDF.]
Correlation between mGA、adjGA、cGA and blood glucose. Correlations between mGA, adjGA, cGA, and glycemic parameters in different albuminuria groups. (A–C) show the correlations between mGA, adjGA, cGA and MBG in the UP1, UP2, and the total population, respectively. (D–F) show the correlations between mGA, adjGA, cGA and FBG in UP1, UP2, and the total population, respectively. (G–I) show the correlations between mGA, adjGA, cGA and HbA1c in UP1, UP2, and the total population, respectively.
Difference between adjusted GA and measured GA
To illustrate the scatter plot and generate a curve for fitting, the difference between adjGA and mGA (△GA) was used as the y-axis and 24hUP as the x-axis. The difference between adjGA and mGA was positively linked with 24hUP and the overall fitted curve was △GA = 0.0011 × 24hUP + 1.1214 (R2 = 0.7069, P < 0.001), but in UP1 the fitted curve was△GA = 0.0017 × 24hUP + 0.2799 (R2 = 0.576, P < 0.001), however, in UP2 the fitted curve was △GA = 0.0009 × 24hUP + 2.8879 (R2 = 0.3986, P < 0.001). The UP subgroups had a different curve shape and degree of data dispersion (Fig. 3).
Fig. 3 [Images not available. See PDF.]
Relationship between ∆GA and 24hUP in UP subgroups. The Figure reflects the relationship between ∆GA and 24hUP, where ∆GA linearly increases as albuminuria levels rise.
The attainmentattainment of glycemic control specified by AdjGA, mGA
The regression curve of adjGA vs. HbA1c was generated in the non-anemic and non-low protein population of 50 cases: adjGA = 2.767×HbA1c + 0.254 (R2 = 0.646, P < 0.001), from which the matching adjGA attainment value was determined. The regression curve of mGA vs. HbA1c was created in a non-anemic and non-low protein population of 50 cases: mGA = 2.598×HbA1c-0.448 (R2 = 0.561, P < 0.001), from which the corresponding mGA attainment values were determined. In the G1-G3a period, the regression equations produced ideal values of 19.04% and below for mGA and 21.00% and below for adjGA, and 21.64% and below for mGA and 23.77% and below for adjGA in the G3b-G5 era.
The glucose compliance rate indicated by mGA was 75.4% among all participants, which was considerably higher than the glucose compliance rate estimated by adjGA, which was 52.8% (χ2 = 55.415, P < 0.001) (data not shown). There was a significant difference in the glucose compliance rate measured by mGA in the albumin and urine protein subgroups (P < 0.001) and no difference in the glucose compliance rate determined by adjGA (P > 0.05) (Fig. 4).The data are shown in Table 7.
Fig. 4 [Images not available. See PDF.]
Comparison of Glycemic Attainment Rates Between adjGA and mGA and Their Association with HbA1c, Hypoproteinemia, and Macroalbuminuria. Panels A and B show the regression analysis between HbA1c and adjGA (A), and HbA1c and mGA (B), used to establish target attainment thresholds.Panels C and D illustrate the attainment rates of glycemic control based on mGA in subgroups with and without hypoproteinemia (C) and macroalbuminuria (D).Panels E and F display the attainment rates based on adjGA in subgroups with and without hypoproteinemia (E) and macroalbuminuria (F).
Table 7. Glucose attainment rates specified by mGA and AdjGA in different subgroups.
Variables | Serum albumin subgroups | Urine protein subgroups | |||
|---|---|---|---|---|---|
SA1(n = 77) | SA2(n = 118) | UP1 (n = 108) | UP2 (n = 87) | ||
mGA | Attainment rate | 46(59.70%) | 101(85.60%) | 63(58.30%) | 84(96.60%) |
χ2 | 16.783 < 0.001 | 37.928 | |||
P-value | < 0.001 | ||||
adjGA | Attainment rate | 38(49.40%) | 65(55.10%) | 56(51.90%) | 47(54.00%) |
χ2 | 0.615 | 0.091 | |||
P-value | 0.524 | 0.875 | |||
A two-sided P-value < 0.05 was considered statistically significant.
Discussion
Albumin has a half-life of around 15 days, a glycation rate of 4.5 times that of hemoglobin26, and non-enzymatic glycosylation of serum albumin accounts for about 80% of all glycosylation of circulating proteins. The buildup of glycosylation products over time may result in gradual degradation of renal function. Degraded glycosylation products are broken down into soluble small molecule peptides, which reenter the circulation and are eliminated in the urine27. GA is a potential marker of glycemic control due to its short half-life, high glycosylation rate, and high serum albumin content. It may be used to measure the effectiveness of diabetic medication therapy and short-term changes in glycemic control.
For a variety of reasons, there is currently no highly accurate technique for measuring GA in patients. First, there are no defined processes for GA measurement techniques, thus we employ the Lucica GA-L enzyme method, which is one of the most regularly used GA assays in theclinic. Second, in addition to blood glucose levels, albumin turnover metabolism has a significant impact on glycated albumin readings. Low levels of GA are seen in NS, Cochin syndrome, etc15,28. High GA levels are observed in disorders characterized by insufficient albumin metabolism, such as cirrhosis and hypothyroidism29,30. Third, studies have found a link between GA and body mass index (BMI), uric acid levels, lipid levels, inflammatory response, and smoking history31, 32, 33, 34–35; however, the underlying association is unclear. DKD patients exhibit metabolic dysfunction, which may have an effect on GA readings.
GA, on the other hand, is less impacted in patients with CKD than glycated hemoglobin, hence GA should be used to monitor blood glucose levels in patients with CKD(Supplementary Material). However, this does not imply that mGA in CKD is an acceptable response to glycemic state. Previous clinical research studies on GA have mostly focused on healthy people, DM patients without complications, or DM patients on dialysis, with essentially no clinical evaluation of GA test results in patients with renal insufficiency and macroalbuminuria. We aimed to assess GA’s clinical reliability and optimize the methodology.
To date, evidence suggests that GA offers a more accurate reflection of glycemic control in patients with advanced CKD and serves as a reliable predictor of mortality in dialysis patients16,36. Although the specific adjustment formulas employed in these studies differ, they are unified by a common approach: to account for the variability introduced by fluctuations in serum albumin levels.
Our adjusted GA was based on the turnover metabolism of albumin (shown in Fig. 5), optimizing Fei Y correction formula. Patients with G3A3 stage had a median eGFR of 41.22 mL/min/1.73 m2 and a median UACR of 3051.57 mg/g, in our research. Our findings indicate that the capacity of mGA to assess blood glucose levels is impaired in DKD, with macroalbuminuria being one of the likely reasons. The adjGA is more sensitive to blood glucose than the cGA. Correlation study revealed that non-glycemic factors such as 24hUP and ALB influenced mGA, cGA was influenced by non-glycemic factors such as 24hUP and SCr, and adjGA was unaffected by these factors other than glycemia. Regression study revealed that adjGA had a stronger connection with blood glucose than mGA. The chi-square analysis also revealed that in situations of disrupted albumin metabolism, such as macroalbuminuria or hypoproteinemia, mGA dramatically overestimated the glycemic profile, but adjGA did not.
Fig. 5 [Images not available. See PDF.]
Albumin metabolic process in the normal human body and diabetic kidney disease patients. The figure displays albumin metabolic process in the normal human body and diabetic kidney disease patients. The bulk of albumin is created in the liver and delivered into the circulation as protein particles. Albumin exchanges with the interstitial fluid in the circulation via the pores of blood vessel walls and capillaries and diffuses into the interstitial fluid. The lymphatic system collects the interstitial fluid albumin and returns it to the circulation, whereas the old albumin is eventually removed by the liver. In the presence of DKD and albuminuria, the turnover rate of albumin increases while the turnover time shortens, and the liver’s production and clearance capacities of albumin are increased. Some albumin is also excreted in the form of proteinuria. Furthermore, the volume ratio of albumin in plasma and interstitial fluid changes, with a greater proportion of albumin diffusing into the interstitial fluid. This alteration leads to a 2:3 ratio of albumin in plasma and interstitial fluid.
We performed a curve fit of the difference between mGA and adjGA to 24hUP, which showed that a 24hUP of 3500 mg/24 h can be considered an inflection point for the reliability of GA validity. The mGA is reasonably reliable for the response of recent blood glucose levels below 24hUP 3500 mg/24 h; however, above 24hUP 3500 mg/24 h, the mGA tends to underestimate blood glucose levels. Our adjGA based on albumin turnover also performed better than non-macroalbuminuria, showing that mGA is overestimated to a greater extent as the patient’s albuminuria grows, implying that adjGA is more significant in persons with macroalbuminuria than in people without macroalbuminuria.
When compared to mGA, adjGA corrects for the drop in GA induced by albuminuria. Additionally, adjGA is always superior in correlation with blood glucose levels and can better represent the patient’s blood glucose levels. When albuminuria exceeds 3500 mg/24 h, mGA is imbalanced in measuring blood glucose levels and does not correspond with the patient’s blood glucose levels due to numerous metabolic variables and albumin excretion. This clearly suggests that mGA cannot be utilized to measure blood glucose levels in DKD patients with macroalbuminuria. However, adjGA largely compensates for this short-coming, and it can, to some extent, represent the blood glucose level of DKD patients with macroalbuminuria, independent of the degree of albuminuria. To some extent, the adjGA can represent a patient’s blood glucose level and serve as a reference for clinical practice.
The median mGA in macroalbuminuria group was 13.20%, which was significantly lower than the 19.75% in the non-macroalbuminuria group, while FBG, MBG, and HbA1c were lower in the macroalbuminuria group than the non-macroalbuminuria group, and there was no difference in adjGA. This might be connected to more stringent glucose control by patients and clinicians. The reduction in mGA levels of albuminuria in the nephrotic range was not related to glycemic state, according to Tomonari Okada et al.37. Our findings agree with prior publications. The adjGA and FBG do not correlate well in macroalbuminuria. Some researchers have also demonstrated that a significant percentage of T2DM patients in healthcare institutions have variable FBG and HbA1c readings, and that these individuals are not routinely defined, which supports our findings38.
There has been no published research on the particular function of albuminuria con-tent in GA. Giglio R et al.39 proposed that GA levels in CKD patients with non-macroalbuminuria are trustworthy. Due to a lack of relevant evidence, this conclusion could not be proven to be correct, however it is close to our conclusion. In China, some researchers used 3500 mg/24 h as a cut-off to separate patients into macroalbuminuria and non-massive albuminuria groups, however the specific cause for the classification is unclear40. We suspect that the cause of this scenario is connected to albumin synthesis and metabolism41.
DKD patients’ albumin metabolism is also affected to some extent, and a considerable number of DKD patients have low serum albumin, the causes of which remain un-known41. This encompasses both DKD patients’ poor protein metabolism and the altered protein metabolism that happens in the body during macroalbuminuria. Jensen H et al.14 demonstrated that the rate of albumin synthesis in patients with nephrotic syn-drome is much lower than in the rest of the patients, resulting in massive albumin loss, and he speculated that these patients with NS may have some underlying disease or other factors that limit the potential increase in hepatic albumin synthesis. Another explanation is that the excretion of albumin in the urine causes a loss of protein from the body, and most of the protein lost to the intestine is hydrolyzed into amino acids and reabsorbed, including in patients with renal failure, where the rate of hepatic albumin synthesis de-creases substantially, so we believe that in patients with DKD with macroalbuminuria there is also a partial cause of the change in albumin synthesis and catabolism, resulting in serum albumin. Hypoalbuminemia in patients with NS results from excessive urinary loss, decreased hepatic synthesis, and increased rates of albumin catabolism42. We believe there are various causes for alterations in albumin production and catabolism in DKD patients with macroalbuminuria, resulting in a considerable drop in serum albumin and, as a result, an underestimate of GA.
The inflammatory response generated by DKD, on the other hand, changes vascular permeability, resulting in an altered volume of protein distribution14. As a result of in-creased capillary escape of serum albumin and other plasma solutes into the interstitial space and cells, this is also a key factor impacting albumin metabolism in DKD patients. In DKD patients’ kidneys, vascular endothelial growth factor (VEGF) expression is up-regulated, and VEGF expression increases vascular permeability, which results in ab-normal albumin volume distribution14,43,44. Our findings revealed a negative relationship between lipid levels and mGA, but lipid levels did not have an independent im-pact on mGA. We excluded infected individuals and used C-reactive protein (CRP) rather than high-sensitivity C-reactive protein as the reference inflammatory index, therefore the influence of an inflammatory response was not taken into account in our findings.
Researchers have noticed this and examined it at the clinical, in vivo, and ex vivo levels. In vivo research by Bent-Hansen L45 shown that urine protein excretion enhanced the transcapillary escape rate (TER) of albumin. Sayo Ueda et al.46 found that in practice, the effect of hemodialysis-induced albumin leakage on GA is insignificant, while not dismissing the effect of urinary protein loss. Albumin and hemoglobin fight for glycation, with hemoglobin exhibiting higher glycation in hypoalbuminemia. When examining glycation markers, the influence of protein must be considered40. An increasing amount of research implies that albumin turnover leakage and glycation in DKD patients are caused by more complicated pathways. Our adjGA, based on Fei Y.‘s study17, contains new characteristics such as interstitial albumin and glycation rate that were not incorporated in their initial cGA model. cGA was impacted by factors other than blood glucose in our data, and while it has favorable associations with FBG, MBG, and HbA1c, its model fit is poor. In terms of predictive power and model performance, this suggests that adjGA outperforms cGA. In terms of predictive capabilities and model performance, adjGA exceeds cGA.
A comprehensive study was conducted to determine the appropriate threshold for GA in diagnosing diabetes, and the findings revealed that 17.1% is the glycemic control attainment value in diabetic individuals47. However, it was observed that the GA level in DKD patients with macroalbuminuria significantly exceeds this value, rendering it in-applicable as an achievement indicator. The traditional GA criterion is no longer suitable for measuring blood glucose levels in DKD patients, as individuals within the normal glycated albumin range may actually have higher blood glucose levels. Therefore, establishing the normal range of glycated albumin in DKD patients poses a crucial challenge that requires attention. Remarkably, no existing literature has addressed this specific topic, with the exception of research on dialysis patients with ESRD, where GA should be controlled within the 25% range. This range is associated with a relatively high severity rate and survival rate48. It should be noted that HbA1c attainment values for DKD patients are not fixed but rather dependent on renal function. Meanwhile, we believe that GA correction does not affect its usual reference value, although further research is necessary to support our hypotheses.
Limitations are present in our research that must be acknowledged. Firstly, it is essential to recognize that this study was conducted at a single center and is retrospective in nature. Consequently, the small sample size of only 195 cases limited the ability to demonstrate the significance of GA in relation to lipids, inflammation, or other markers. Secondly, the assessment of fasting blood glucose levels in DKD patients was performed only once, and multiple measurements were not taken to calculate the mean value. This was due to the high variability of single-fasting blood glucose measurements. To address this issue, we utilized Liu’s mean blood glucose model to compare the accuracy of mGA versus adjGA. Thirdly, it is important to acknowledge that defining the altered albumin metabolic cycle in advanced DKD was challenging, and as a result, our modified GA may not fully reflect the glycemic condition in these patients.In addition, the plasma volume used in our adjGA calculation was estimated solely based on body weight, which may introduce deviations in the accuracy of the adjustment. In future studies, we plan to adopt more precise methods to measure plasma volume, such as the carbon monoxide (CO) rebreathing technique described by Laura Oberholzer et al., which enables direct determination of hemoglobin mass, red cell volume, plasma volume, and blood volume. Implementing such advanced techniques will help improve the accuracy and reliability of the adjGA correction model49.
These difficulties in adequately defining the changed albumin metabolic cycle may impact the interpretation of our results.GA, as a promising developing glycemic monitor, awaits the implementation of strong quality control standards to eliminate inter-laboratory variance. What degree of GA attainment value in different stages of DKD merits additional investigation. In the future, multi-center, large-sample clinical studies will be required to investigate the GA attainment levels in different DKD stages that are appropriate for the national population.
Conclusions
Macroalbuminuria (albuminuria above 3500 mg/24 h) influences measured GA, resulting in an underestimation of blood glucose levels in DKD patients. The modified formula partially eliminates the influence of albuminuria on GA, allowing doctors to more properly measure patient blood glucose.
Acknowledgements
We thank the patients and staff at the department of Nephrology, Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University.
Author contributions
Authors’ contributions：JX and ZW designed the study protocol, analyzed and interpreted the patient data and was a major contributor in writing the manuscript. ZZ, YL, CS and YM collected and collated the data. DC and ZW contributed to the writing of the manuscript and substantively revised it. LS and YW made a substantial contribution to the design of the work, administered the work and was a contributor in writing the manuscript. JX and ZW contributed equally to this work.
Funding
This work was supported by grants from the Natural Science Foundation of Beijing Province (grant number 7172096) and the Beijing Chinese Medicine Science and Technology Development Fund Project Foundation (grant number JJ-2020-46).
Data availability
The data used to support the findings of this study are available from the corresponding author (YW) upon request.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
The research was in accordance with Declaration of Helsinki. The research scheme was reviewed and approved by the medical ethics committee of Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University, the reference number 2017BL02-047-02.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Abstract
Glycated albumin (GA), a blood glucose monitoring biomarker, is impacted by variables such as albumin turnover and is not entirely relevant throughout diabetic kidney disease (DKD). There is insufficient data to routinely adjust GA measurements. We examined how albuminuria affected clinically measured GA (mGA) and adjusted GA (adjGA). We included 195 patients with DKD, 108 with non-macroalbuminuria and 87 with macroalbuminuria, and adjusted GA based on albumin, albuminuria, and body weight. Subgroups were divided to two groups according to albuminuria and serum albumin levels. The relationship between mGA, adjGA, and glucose was investigated. The optimum GA correction method based on albumin turnover metabolism was investigated: adjGA = mGA×[1+(8×K×UP) ÷ (11×V×SA)]. where K represents the standard metabolic days of albumin (15 days), UP is 24-hour urine protein excretion (g/24 h), V is plasma volume (calculated as 5% of body weight in liters), and SA is serum albumin concentration (g/L). In non-macroalbuminuria, mGA was 19.75% and adjGA was 22.32%, and in macroalbuminuria, mGA was 13.20% and adjGA was 22.45%, the mGA was substantially different across albuminuria categories (P < 0.001), but adjGA was not. HbA1c, 24-h urine protein(24hUP) and serum albumin (ALB) were influencing variables for mGA (P < 0.001), while 24hUP and ALB had no effect on adjGA (P > 0.05). The adjGA had stronger cor-relation with blood glucose than mGA, especially in the context of macroalbuminuria. Macroalbuminuria lowers mGA accuracy. In DKD patients with macroalbuminuria, adjusted GA is a novel indication of glucose monitoring.
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Details
1 Department of Nephrology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, 100010, Beijing, China (ROR: https://ror.org/013xs5b60) (GRID: grid.24696.3f) (ISNI: 0000 0004 0369 153X); Beijing University of Chinese Medicine, 100029, Beijing, China (ROR: https://ror.org/05damtm70) (GRID: grid.24695.3c) (ISNI: 0000 0001 1431 9176)
2 Beijing University of Chinese Medicine, 100029, Beijing, China (ROR: https://ror.org/05damtm70) (GRID: grid.24695.3c) (ISNI: 0000 0001 1431 9176); Dongzhimen Hospital Beijing University of Chinese Medicine, 100700, Beijing, China (ROR: https://ror.org/05damtm70) (GRID: grid.24695.3c) (ISNI: 0000 0001 1431 9176)
3 Community Health Service Center, 100067, Beijing, China
4 Department of Nephrology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, 100010, Beijing, China (ROR: https://ror.org/013xs5b60) (GRID: grid.24696.3f) (ISNI: 0000 0004 0369 153X)
5 Faculty of Life Science and Medicine, Northwest University, 710127, Xi’an, China (ROR: https://ror.org/00z3td547) (GRID: grid.412262.1) (ISNI: 0000 0004 1761 5538)
6 Fangshan Hospital, Beijing University of Chinese Medicine, 102400, Beijing, China (ROR: https://ror.org/05damtm70) (GRID: grid.24695.3c) (ISNI: 0000 0001 1431 9176)