GLYCATED ALBUMIN AT 4 WEEKS CORRELATES WITH A1C LEVELS AT 12 WEEKS AND REFLECTS SHORT-TERM GLUCOSE FLUCTUATIONS

Glycated albumin may have a complementary role to glycated hemoglobin in glucose monitoring in childhood acute leukemia

PURPOSE

Glycated hemoglobin (HbA1c) as a glycemic index may have limited value in pediatric patients with acute leukemia as they often present with anemia and/or pancytopenia. To address this issue, we evaluated the usefulness of glycated albumin (GA) as a glycemic monitoring index in pediatric patients with acute leukemia.

METHODS

Medical records of 25 patients with type 2 diabetes mellitus (T2DM), 63 patients with acute leukemia, and 115 healthy children from Seoul St. Mary's Hospital, The Catholic University of Korea, were retrospectively investigated for serum GA, HbA1c, and fasting blood glucose (FBG) levels, along with demographic data.

RESULTS

GA, HbA1c, and FBG levels did not differ between the control and acute leukemia groups. In the T2DM group, positive correlations were observed among GA, HbA1c, and FBG (P<0.01). Although GA level was not associated with the HbA1c level in the control group, GA and HbA1c levels showed a positive correlation in the acute leukemia group (P=0.045). Regression analysis revealed GA and HbA1c levels to be positively correlated in the acute leukemia and T2DM groups even after adjusting for age, sex, and body mass index z-score (P=0.007, P<0.01).

CONCLUSION

GA may be a useful complementary parameter to HbA1c for glycemic monitoring in pediatric patients with acute leukemia, similar to its use in patients with T2DM.

Association of Fructosamine Levels With Glycemic Management in Children With Type 1 Diabetes as Determined by Continuous Glucose Monitoring: Results From the CGM TIME Trial

OBJECTIVE

Our aim in this study was to determine the correlation between serum fructosamine and average blood glucose, as measured by continuous glucose monitoring (CGM) in children with type 1 diabetes.

METHODS

Ninety-seven blood samples were collected from 70 participants in the Timing of Initiation of continuous glucose Monitoring in Established pediatric diabetes (CGM TIME) Trial. Each eligible participant had 3 weeks of CGM data with at least 60% CGM adherence before blood collection. Ordinary least-squares linear regression incorporating restricted cubic splines was used to determine the association between fructosamine levels and mean blood glucose.

RESULTS

An association was found between fructosamine and mean blood glucose, with an F statistic of 9.543 (p<0.001). Data were used to create a formula and conversion chart for calculating mean blood glucose from fructosamine levels for clinical use.

CONCLUSIONS

There is a complex relationship between average blood glucose, as determined by CGM and fructosamine. Fructosamine levels may be clinically useful for assessing short-term glycemic management when CGM is not available.

Usefulness of glycated albumin level as a glycemic index complementing glycosylated hemoglobin in diabetic children and adolescents

PURPOSE

Glycated albumin (GA) is a glycemic marker reflecting the average serum glucose of the previous 2 weeks. This study aimed to evaluate the usefulness of GA as a glycemic index to complement glycosylated hemoglobin (HbA1c) in children and adolescents.

METHODS

Fifty-four children and adolescents with diabetes mellitus (DM) and 97 children and adolescents without DM (NDM) were enrolled. The correlation between mean blood glucose (MG) and GA compared to HbA1c was investigated in the DM group. The correlation between fasting glucose (FG) and GA compared to HbA1c was investigated in the NDM group. Factors affecting GA, HbA1c, and GA/HbA1c were analyzed.

RESULTS

In the DM group, positive correlations were observed between MG and GA (P=0.003), between MG and HbA1c (P=0.001), and between GA and HbA1c (P<0.001). The correlation coefficient between MG and GA did not differ from that between MG and HbA1c in the DM group (P=0.811). Among patients with DM, those whose standardized body mass index standard deviation score (BMI SDS) was ≥2 had a lower GA/HbA1c compared with those whose BMI SDS was <2 (P=0.001). In the NDM group, there were no significant correlations between FG and GA, between FG and HbA1c, or between GA and HbA1c. The NDM subjects whose BMI SDS was ≥2 had a lower GA/HbA1c than did the NDM subjects whose BMI SDS was <2 (P=0.003).

CONCLUSION

GA is comparable with HbA1c in reflecting glycemic control in children and adolescents with DM. GA is affected by obesity in children and adolescents with or without DM.

Glycated Albumin Correlates With Time-in-Range Better Than HbA1c or Fructosamine

CONTEXT

Intermediate-term glycemic control metrics may represent a viable alternative to continuous glucose monitoring (CGM) in patients without access to CGM.

OBJECTIVE

This work aimed to compare the relationship between CGM parameters and glycated albumin (GA), glycated hemoglobin A1c (HbA1c), and fructosamine for 24 weeks.

METHODS

We conducted exploratory comparative analyses of CGM subgroup data from a previously published 24-week prospective study of assay performance in 8 US clinics. Participants included 34 individuals with type 1 (n = 18) and type 2 diabetes (n = 16) undergoing changes to improve glycemic control (n = 22; group 1) or with stable diabetes therapy (n = 12; group 2). Main outcome measures included Pearson correlations between CGM and glycemic indices and receiver operating characteristic (ROC) analysis of glycemic index values predictive of time in range (TIR) greater than 70%.

RESULTS

At weeks 4 and 8, GA correlations with TIR were higher than HbA1c correlations in group 1. In group 2, GA correlations with TIR were statistically significant, whereas HbA1c correlations were not. In both groups over the first 12 weeks, GA correlations with TIR were higher than fructosamine-TIR correlations. In the ROC analysis, GA predicted a TIR greater than 70% during weeks 2 to 24 (area under the curve >0.80); HbA1c was predictive during weeks 12 to 24. Cutoff values for TIR greater than 70% were 17.5% (sensitivity and specificity, 0.88) for GA and 7.3% (0.86) for HbA1c.

CONCLUSION

GA is the most accurate predictor of TIR over 8 weeks compared with other glycemic indices, which may assist in clinical evaluation of changes in treatment where CGM is not possible and it is too early to use HbA1c (NCT02489773).

Protein glycation in diabetes mellitus

Diabetes mellitus is the ninth leading cause of mortality worldwide. It is a complex disease that manifests as chronic hyperglycemia. Glucose exposure causes biochemical changes at the proteome level as reflected in accumulation of glycated proteins. A prominent example is hemoglobin A1c (HbA1c), a glycated protein widely accepted as a diabetic indicator. Another emerging biomarker is glycated albumin which has demonstrated utility in situations where HbA1c cannot be used. Other proteins undergo glycation as well thus impacting cellular function, transport and immune response. Accordingly, these glycated counterparts may serve as predictors for diabetic complications and thus warrant further inquiry. Fortunately, modern proteomics has provided unique analytic capability to enable improved and more comprehensive exploration of glycating agents and glycated proteins. This review broadly covers topics from epidemiology of diabetes to modern analytical tools such as mass spectrometry to facilitate a better understanding of diabetes pathophysiology. This serves as an attempt to connect clinically relevant questions with findings of recent proteomic studies to suggest future avenues of diabetes research.

Analytical performances of a glycated albumin assay that is traceable to standard reference materials and reference range determination

BACKGROUND

Glycated albumin (GA) is an intermediate-term marker for monitoring glycemic control (preceding 2-3 weeks) in patients with diabetes mellitus. We evaluated the performance of Lucica Glycated Albumin-L, a new GA assay that is traceable to standard reference materials and determined the reference range in healthy subjects without diabetes.

METHODS

The performance and reference range studies were conducted in accordance with Clinical and Laboratory Standards Institute (CLSI) Guidelines. The traceability was established using reference material recommended by the Japan Society of Clinical Chemistry (JSCC).

RESULTS

The coefficient of variation (CV) of overall repeatability, within-laboratory precision, and overall reproducibility values of GA values were not more than 2.6%, 3.3%, and 1.6%, respectively, among laboratories. The GA values showed good linearity from 173 to 979 mmol/mol (9.4%-54.9%) across the assay range. The GA reference range in 262 healthy subjects was between 183 and 259 mmol/mol (9.9%-14.2%) while that of subjects with diabetes was 217-585 mmol/mol (11.8-32.6%). The reagent was stable for 2 months on the bench at room temperature. The limits of blank, detection, and qualification were 6.9, 7.9, and 9.7 μmol/L for GA concentration, and 3.8, 7.0, and 21.8 μmol/L for albumin concentration, respectively. Hemoglobin slightly affected the assay, while other classical interfering substances had no significant impact.

CONCLUSIONS

The present GA assay shows comparable performance to current clinical assays and could be used for intermediate-term monitoring of glycemic control in diabetes patients.

Glycated albumin as a biomarker for diagnosis of diabetes mellitus: A systematic review and meta-analysis

BACKGROUND

Glycated albumin (GA), the non-enzymatic glycation product of albumin in plasma, became a glycemic marker in the beginning of the 21st century. The assay is not affected by hemoglobin levels and reflects the glycemic status over a shorter period as compared to HbA1c measurements. Thus, GA may contributes as an intermediate glucose index in the current diabetes mellitus (DM) diagnostic system.

AIM

To search and summarize the available data on glycated albumin measurements required for the diagnosis of diabetes mellitus.

METHODS

Databases, including PubMed, Embase, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL), among others, were systematically searched. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was applied for the assessment of quality, and the bivariate model was used to pool the sensitivity and specificity. The hierarchical summary receiver operator characteristic curves (HSROC) model was utilized to estimate the summary receiver operating characteristics curve (SROC). Sensitivity analysis was performed to investigate the association of the study design and patient characteristics with the test accuracy and meta-regression to find the source of heterogeneity.

RESULTS

Three studies regarding gestational diabetes mellitus (GDM) and a meta-analysis of 16 non-GDM studies, comprising a total sample size of 12876, were included in the work. Results reveal that the average cut-off values of GA reported for the diagnosis of GDM diagnosis was much lower than those for non-GDM. For non-GDM cases, diagnosing DM with a circulating GA cut-off of 14.0% had a sensitivity of 0.766 (95%CI: 0.539, 0.901), specificity of 0.687 (95%CI: 0.364, 0.894), and area under the curve of 0.80 (95%CI: 0.76, 0.83) for the SROC. The estimated SROC at different GA cut-off values for non-GDM exhibited that the average location parameter lambda of 16 non-GDM studies was 2.354 (95%CI: 2.002, 2.707), and the scale parameter beta was -0.163 (95%CI: -0.614, 0.288). These non-GDM studies with various thresholds had substantial heterogeneity, which may be attributed to the type of DM, age, and body mass index as possible sources.

CONCLUSION

Glycated albumin in non-DM exhibits a moderate diagnostic accuracy. Further research on the diagnostic accuracy of GA for GDM and combinational measurements of GA and other assays is suggested.

Glycated albumin as biomarker: Evidence and its outcomes

Glycemic control markers are important for the diagnosis and treatment of diabetes. Hemoglobin A1c (A1C) is an important marker that is mandatory in routine medical examinations; however, it is well known that it has some limitations. In this review, we focus on the limitation of A1C and introduce a relatively new marker, glycated albumin (GA), which can be used to complement A1C. First, for a better understanding of the characteristics of each marker, we sort the similarities and differences of glycemic control markers as well as the characteristics of each marker. Second, we point out the limitation of A1C, introduce GA as an alternative indicator, and discuss the limitations of GA. Finally, we summarize important evidence regarding the utility of GA. We hope that this review provides useful information that permits more effective usage of GA as well as other glycemic control markers.

Time in range correlates glycated albumin measured immediately after 2 weeks of continuous glucose monitoring

AIMS

Time in range (TIR), an index of glycemic control and also blood glucose fluctuation, obtained from continuous glucose monitoring (CGM), has been increasing its importance along with the spread of CGM in recent years. For a while, glycated albumin (GA) has been also used as a glycemic control index during about 2-weeks in routine clinical practice. It has not yet been confirmed under optimal condition whether TIR and GA correlates. Clarification of the correlation between TIR and GA, which was measured immediately after 2-weeks of CGM, might be a finding that further supports the utility of TIR.

METHODS

GA was measured at the conclusion of 2-week CGM in 71 diabetes outpatients at our hospital, and the correlation between GA and indices such as TIR obtained from CGM was statistically analyzed.

RESULTS

It was found that TIR and time above range (TAR) were significantly correlated with GA. Upon performing multiple regression analysis, TIR, TAR and BMI. indicated a significant regression coefficient with respect to GA.

CONCLUSIONS

These findings further support the utility of TIR as a marker of glycemic control that it might also be correlated with GA, and also suggest a relation between GA and blood glucose fluctuation.

Improved individual and population-level HbA1c estimation using CGM data and patient characteristics

Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.

Quantitation of glycated albumin by isotope dilution mass spectrometry

BACKGROUND

Glycated albumin is considered an alternative glycemic indicator in certain situations where HbA1c does not accurately reflect glycemic status. These patient cases are usually associated with decreased erythrocyte lifespan, gestational diabetes, or end-stage renal disease. The aim of our study was to develop an assay for absolute quantitation of glycated albumin based on isotope dilution liquid chromatography-mass spectrometry.

METHODS

The plasma samples were reduced/alkylated, spiked with isotope-labeled standards RQIKKQTALV(D₈)E and RQIKK(fructosyl)QTALV(D₈)E and enzymatically digested by Glu-C. The samples were analyzed on an LC-MS system. Two MRM transitions (M³⁺ → (b₉-3H₂O)²⁺ and M³⁺ → (b₁₀-3H₂O)²⁺ or M³⁺ → b₉²⁺ and M³⁺ → b₁₀²⁺) were used for each peptide, then the percentage of glycation (MS GA%) was calculated.

RESULTS

The comparison study demonstrated a good linear correlation between our LC-MS/MS and Lucica method with r² = 0.95. The intra-day CV for the low HbA1c sample was 2.2%, while CV for the high HbA1c sample was 0.64%. Inter-day CV for low HbA1c sample was 5.6%, while the CV for the high HbA1c sample was 5.7%. We found the LLOQ to be 0.12 nmol/ml for the non-glycated and glycated peptide. No interference from hemoglobin was observed up to 500 mg/dL concentration.

CONCLUSIONS

This is the first implementation of isotope dilution LC-MS assay for glycated albumin with simultaneously quantitation of glycated and non-glycated peptides. The method includes a simple sample preparation and has demonstrated a good analytical performance.

Comparison of Premixed Human Insulin 30/70 to Biphasic Aspart 30 in Well-Controlled Patients with Type 2 Diabetes Using Continuous Glucose Monitoring

AIM

To compare in terms of glycemic variability two premixed insulins, Premixed Human Insulin 30/70 (PHI) and Biphasic Aspart 30 (BiAsp30), using Continuous Glucose Monitoring (CGM) and to estimate the correlation of Glycated Albumin (GA) and Fructosamine (FA) with CGM data. Patients-Data: A total of 36 well-controlled patients with type 2 Diabetes Mellitus (T2DM) underwent 7-day CGM with PHI and subsequently with BiAsp30. GA and FA were measured at the first and last day of each week of CGM.

RESULTS

BiAsp30 was associated with lower Average Blood Glucose (ABG) during the 23:00-03:00 period (PHI: 135.08 ± 28.94 mg/dL, BiAsp30: 117.75 ± 21.24 mg/dL, p < 0.001) and the 00:00-06:00 period (PHI: 120.42 ± 23.13 mg/dL, BiAsp30: 111.17 ± 14.74 mg/dL, p = 0.008), as well as with more time below range (<70 mg/dL) (TBR) during the 23:00-03:00 period in the week (PHI: 3.65 ± 5.93%, BiAsp30: 11.12 ± 16.07%, p = 0.005). PHI was associated with lower ABG before breakfast (PHI: 111.75 ± 23.9 mg/dL, BiAsp30: 128.25 ± 35.9 mg/dL, p = 0.013). There were no differences between the two groups in ABG, Time In Range and Time Below Range during the entire 24-h period for 7 days, p = 0.502, p = 0.534, and p = 0.258 respectively, and in TBR for the 00:00-06:00 period p = 0.253. Total daily insulin requirements were higher for BiAsp30 (PHI: 47.92 ± 12.18 IU, BiAsp30: 49.58 ± 14.12 IU, p = 0.001). GA and FA correlated significantly with ABG (GA: r = 0.512, p = 0.011, FA: r = 0.555, p = 0.005).

CONCLUSIONS

In well-controlled patients with T2DM, BiAsp30 is an equally effective alternative to PHI.

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.

Results of a Study Comparing Glycated Albumin to Other Glycemic Indices

CONTEXT

Intermediate-term glycemic control metrics fulfill a need for measures beyond hemoglobin A1C.

OBJECTIVE

Compare glycated albumin (GA), a 14-day blood glucose measure, with other glycemic indices.

DESIGN

24-week prospective study of assay performance.

SETTING

8 US clinics.

PARTICIPANTS

Subjects with type 1 (n = 73) and type 2 diabetes (n = 77) undergoing changes to improve glycemic control (n = 98) or with stable diabetes therapy (n = 52).

INTERVENTIONS

GA, fructosamine, and A1C measured at prespecified intervals. Mean blood glucose (MBG) calculated using weekly self-monitored blood glucose profiles.

MAIN OUTCOME MEASURES

Primary: Pearson correlation between GA and fructosamine. Secondary: magnitude (Spearman correlation) and direction (Kendall correlation) of change of glycemic indices in the first 3 months after a change in diabetes management.

RESULTS

GA was more concordant (60.8%) with changes in MBG than fructosamine (55.5%) or A1C (45.5%). Across all subjects and visits, the GA Pearson correlation with fructosamine was 0.920. Pearson correlations with A1C were 0.655 for GA and 0.515 for fructosamine (P < .001) and with MBG were 0.590 and 0.454, respectively (P < .001). At the individual subject level, Pearson correlations with both A1C and MBG were higher for GA than for fructosamine in 56% of subjects; only 4% of subjects had higher fructosamine correlations with A1C and MBG. GA had a higher Pearson correlation with A1C and MBG in 82% and 70% of subjects, respectively.

CONCLUSIONS

Compared with fructosamine, GA correlates significantly better with both short-term MBG and long-term A1C and may be more useful than fructosamine in clinical situations requiring monitoring of intermediate-term glycemic control (NCT02489773).

Search for clinical predictors of good glycemic control in patients starting or intensifying oral hypoglycemic pharmacological therapy: A multicenter prospective cohort study

AIMS

Our aim was to search for clinical predictors of good glycemic control in patients starting or intensifying oral hypoglycemic pharmacological therapy.

METHODS

A multicenter, prospective cohort of 499 diabetic subjects was enrolled in this study: patients with newly diagnosed diabetes (NDM group) or poor glycemic control with oral antidiabetic drugs (OADs) (PDM group). All subjects then started or intensified OADs therapy and followed up for 91 days. Glycemic control was determined according to HbA1c at day 91 with HbA1c <7% considered good.

RESULTS

The proportions of patients with good glycemic control after follow up for 91 days were 66.9% and 34.8% in NDM group and PDM group respectively. Logistic regression analysis showed that the change in GA at 28 days was the only predictor of good glycemic control in NDM patients (OR = 1.630, 95% CI 1.300-2.044, P < 0.001). In PDM patients, changes in GA at 28 days, CPI, baseline HbA1c, diabetic duration, and BMI were all independent predictors of good glycemic control (All P < 0.05).

CONCLUSIONS

GA decline is a good predictor of future success in newly diagnosed patients. In patients intensifying therapy, beside GA decline, other individualized clinical characteristics should also be considered.

2019 John Insall Award: Fructosamine is a better glycaemic marker compared with glycated haemoglobin (HbA1C) in predicting adverse outcomes following total knee arthroplasty: a prospective multicentre study

AIMS

The best marker for assessing glycaemic control prior to total knee arthroplasty (TKA) remains unknown. The purpose of this study was to assess the utility of fructosamine compared with glycated haemoglobin (HbA1c) in predicting early complications following TKA, and to determine the threshold above which the risk of complications increased markedly.

PATIENTS AND METHODS

This prospective multi-institutional study evaluated primary TKA patients from four academic institutions. Patients (both diabetics and non-diabetics) were assessed using fructosamine and HbA1c levels within 30 days of surgery. Complications were assessed for 12 weeks from surgery and included prosthetic joint infection (PJI), wound complication, re-admission, re-operation, and death. The Youden's index was used to determine the cut-off for fructosamine and HbA1c associated with complications. Two additional cut-offs for HbA1c were examined: 7% and 7.5% and compared with fructosamine as a predictor for complications.

RESULTS

Overall, 1119 patients (441 men, 678 women) were included in the study. Fructosamine level of 293 µmol/l was identified as the optimal cut-off associated with complications. Patients with high fructosamine (> 293 µmol/l) were 11.2 times more likely to develop PJI compared with patients with low fructosamine (p = 0.001). Re-admission and re-operation rates were 4.2 and 4.5 times higher in patients with fructosamine above the threshold (p = 0.005 and p = 0.019, respectively). One patient (1.7%) from the elevated fructosamine group died compared with one patient (0.1%) in the normal fructosamine group (p = 0.10). These complications remained statistically significant in multiple regression analysis. Unlike fructosamine, all three cut-offs for HbA1c failed to show a significant association with complications.

CONCLUSION

Fructosamine is a valid and an excellent predictor of complications following TKA. It better reflects the glycaemic control, has greater predictive power for adverse events, and responds quicker to treatment compared with HbA1c. These findings support the screening of all patients undergoing TKA using fructosamine and in those with a level above 293 µmol/l, the risk of surgery should be carefully weighed against its benefit. Cite this article: Bone Joint J 2019;101-B(7 Supple C):3-9.

Glycated albumin as a glycaemic marker in patients with advanced chronic kidney disease and anaemia: a preliminary report

Background: The association between glycated albumin (GA) and glycaemic status has not been fully described in patients with advanced chronic kidney disease (CKD) in relation to anaemia. The aim of this study was to evaluate the relationship between GA and fasting plasma glucose (FPG) and HbA1c in patients with advanced CKD and to evaluate the influence of anaemia in such relationship. Materials and methods: Patients with CKD stage 4 or 5 were included in the study. eGFR was calculated by the CKD-EPI creatinine equation. Plasma GA was measured by an enzymatic method. Results: Eighty-one patients were included in the study, 46 (57%) were males; the mean age was 67 ± 14 years. HbA1c was correlated with Hb (r = 0.39; p = .0003), and no significant correlation was detected between plasma GA and serum albumin (p = .82). A significant association between FPG and GA (r² = 0.41; p < .0001), and between FPG and HbA1c (r² = 0.42; p < .0001) was detected in the whole study population. Patients with moderate/severe anaemia had lower HbA1c than patients with no anaemia, while both FPG and GA were comparable between the two groups. Multivariate regression analysis showed that GA was a significant predictor of FPG in patients with moderate/severe anaemia while HbA1c did not (r² = 0.55; p < .0001 for the model). Conclusions: GA, alone or in combination with other biomarkers, can be considered for the evaluation of glycaemic status in patients with advanced CKD and severe anaemia.

Glycated albumin is correlated with glycated hemoglobin in type 2 diabetes

Background and aims

Glycated hemoglobin (HbA1c) retrospectively evaluates mean glycemia in the preceding 2–3 months and is the gold standard for assessing glycemic control, while glycated albumin (GA) is currently considered a short to intermediate term integrated glycemic control marker, since it reflects glycemic status over the last 3 weeks. We aimed to investigate the levels of GA, HbA1c and fasting glycemia in a group of patients with type 2 diabetes.

Methods

The observational study included adult type 2 diabetes patients (n=135) according to inclusion and exclusion criteria, randomly selected from Clinical Centre of Diabetes, Cluj-Napoca, Romania. Fasting glycemia, GA, HbA1c and creatinine were measured using commercially available methods.

Results

Of the whole group, 62 (45.9%) were men. Mean age was 62.1±8.6 years old, body mass index was 31.8±6.1 kg/m2 and diabetes duration was 10.0 (4.0; 15.0) years. Fasting glycemia was 162±13.7 mg/dl, GA was 28.0 (21.0; 40.0)% and HbA1c 8.9±2.3%. We found GA was significantly correlated with HbA1c (r=0.19; p=0.029) and fasting glycemia (r=0.32; p<0.001), while HbA1c was significantly correlated with fasting glycemia (r=0.40; p<0.001).

Conclusions

GA was significantly correlated with both HbA1c and fasting glycemia in our patients with type 2 diabetes. While HbA1c is recognized as being the reference test for diabetes control monitoring, GA might a useful biomarker for assessing short to intermediate term glycemic control, particularly important in situations when HbA1c test cannot be reliable or earlier clinical decision making is mandatory.

Incretin-based therapies for patients with type 1 diabetes: a meta-analysis

OBJECTIVE

Combined treatment with an incretin-based drug, such as a glucagon-like peptide 1 receptor agonist (GLP-1 RA) or a dipeptidyl peptidase-4 (DPP-4) inhibitor, and basal insulin is a new strategy for improving glucose control in type 1 diabetes mellitus (T1DM). We performed a meta-analysis to assess the effect of this combined treatment on glycaemic control, insulin dose, severe hypoglycaemia, weight gain and gastrointestinal side effects in T1DM patients.

METHODS

We searched PubMed, EMBASE and the Cochrane Library for relevant studies published before July 16, 2018. The primary outcome was glycosylated haemoglobin (HbA1c). Secondary outcomes included total daily insulin dose, body weight, severe hypoglycaemia and gastrointestinal side effects.

RESULTS

Nine randomized controlled trials (RCTs) involving 2389 patients were ultimately included in the meta-analysis. The pooled data suggested that incretin-based therapy was associated with a reduction in HbA1c levels (weighted mean difference (WMD) -0.17%, 95% confidence interval (CI) -0.24 to -0.11, P < 0.001), total daily insulin dose (WMD -5.53 IU/day, 95% CI -8.89 to -2.17, P = 0.001) and body weight (WMD -3.24 kg, 95% CI -4.43 to -2.04, P < 0.001). Incretins did not increase the risk of severe hypoglycaemia (odds ratio (OR) 0.83, 95% CI 0.60-1.16, P = 0.287) but increased the occurrence of gastrointestinal side effects (OR 3.46, 95% CI 2.20-5.45, P < 0.001).

CONCLUSIONS

In T1DM patients, GLP-1 RAs, but not DPP-4 inhibitors, combined with insulin appear to be an effective therapy but may increase the occurrence of gastrointestinal side effects.

The clinical usefulness of glycated albumin in patients with diabetes and chronic kidney disease: Progress and challenges

Prolonged hyperglycemia leads to a non-enzymatic glycation of proteins, and produces Amadori products, such as glycated albumin (GA) and glycated hemoglobin (HbA1c). The utility of HbA1c in the setting of chronic kidney disease (CKD) may be problematic since altered lifespan of red blood cells, use of iron and/or erythropoietin therapy, uremia and so on. Therefore, as an alternative marker, GA has been suggested as a more reliable and sensitive glycemic index in patients with CKD. In addition to the mean plasma glucose concentration, GA also reflects postprandial plasma glucose and glycemic excursion. Besides, with a half-life of approximately 2-3 weeks, GA may reflect the status of blood glucose more rapidly than HbA1c. GA is also an early precursor of advanced glycation end products (AGEs), which cause alterations in various cellular proteins and organelles. Thus, high GA levels may correlate with adverse outcomes of patients with CKD. In this review, the clinical usefulness of GA was discussed, including a comparison of GA with HbA1c, the utility and limitations of GA as a glycemic index, its potential role in pathogenesis of diabetic nephropathy and the correlations between GA levels and outcomes, specifically in patients with diabetes and CKD.

Hemoglobin A1c Accurately Predicts Continuous Glucose Monitoring-Derived Average Glucose in Youth and Young Adults With Cystic Fibrosis

OBJECTIVE

In cystic fibrosis (CF), hemoglobin A_1c (HbA_1c) is thought to underestimate glycemia. However, few studies have directly assessed the relationship between HbA_1c and average glucose in CF. We determined the relationships among glycemic markers-HbA_1c, fructosamine (FA), glycated albumin (%GA), and 1,5-anhydroglucitol (1,5-AG)-and continuous glucose monitoring (CGM) in CF, hypothesizing that alternate markers would better predict average sensor glucose (ASG) than HbA_1c.

RESEARCH DESIGN AND METHODS

CF participants and a group of healthy control subjects (HCs), ages 6-25 years, wore CGM for up to 7 days. Pearson correlations assessed the relationships between CGM variables and HbA_1c, FA, %GA, and 1,5-AG. The regression line between HbA_1c and ASG was compared in CF versus HC. Linear regressions determined whether alternate markers predicted ASG after adjustment for HbA_1c.

RESULTS

CF (n = 93) and HC (n = 29) groups wore CGM for 5.2 ± 1 days. CF participants were 14 ± 3 years of age and 47% were male, with a BMI z score -0.1 ± 0.8 and no different from HCs in age, sex, or BMI. Mean HbA_1c in CF was 5.7 ± 0.8% (39 ± 9 mmol/mol) vs. HC 5.1 ± 0.2% (32 ± 2 mmol/mol) (P < 0.0001). All glycemic markers correlated with ASG (P ≤ 0.01): HbA_1c (r = 0.86), FA (r = 0.69), %GA (r = 0.83), and 1,5-AG (r = -0.26). The regression line between ASG and HbA_1c did not differ in CF versus HC (P = 0.44). After adjustment for HbA_1c, %GA continued to predict ASG (P = 0.0009) in CF.

CONCLUSIONS

HbA_1c does not underestimate ASG in CF as previously assumed. No alternate glycemic marker correlated more strongly with ASG than HbA_1c. %GA shows strong correlation with ASG and added to the prediction of ASG beyond HbA_1c. However, we are not advocating use of HbA_1c for diabetes screening in CF based on these results. Further study will determine whether glycemic measures other than ASG differ among different types of diabetes for a given HbA_1c.

Preoperative Diabetes Optimization Program

IN BRIEF "Quality Improvement Success Stories" are published by the American Diabetes Association in collaboration with the American College of Physicians, Inc., and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes a successful effort to improve glycemic control in presurgical patients with an A1C >8%.

Alternate glycemic markers reflect glycemic variability in continuous glucose monitoring in youth with prediabetes and type 2 diabetes

OBJECTIVE

To determine whether the alternate glycemic markers, fructosamine (FA), glycated albumin (GA), and 1,5-anhydroglucitol (1,5AG), predict glycemic variability captured by continuous glucose monitoring (CGM) in obese youth with prediabetes and type 2 diabetes (T2D).

STUDY DESIGN

Youth with BMI ≥85th%ile, 10-18 years, had collection of fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), FA, GA, and 1,5AG and 72 hours of CGM. Participants with HbA1c ≥5.7% were included. Relationships between glycemic markers and CGM variables were determined with Spearman correlation coefficients. Linear models were used to examine the association between alternate markers and CGM measures of glycemic variability-standard deviation (SD) and mean amplitude of glycemic excursions (MAGE)-after controlling for HbA1c.

RESULTS

Total n = 56; Median (25th%ile, 75th%ile) age = 14.3 years (12.5, 15.9), 32% male, 64% Hispanic, 20% black, 13% white, HbA1c = 5.9% (5.8, 6.3), FA=211 mmol/L (200, 226), GA= 12% (11%, 12%), and 1,5AG = 22mcg/mL (19, 26). HbA1c correlated with average sensor glucose, AUC, SD, MAGE, and %time > 140 mg/dL. FA and GA correlated with average and peak sensor glucose, %time >140 and >200 mg/dL, and MAGE. GA also correlated with SD and AUC180. 1,5AG correlated with peak glucose, AUC180, SD, and MAGE. After adjusting for HbA1c, all 3 markers independently predicted MAGE; FA and GA independently predicted SD.

CONCLUSIONS

Alternate glycemic markers predict glycemic variability as measured by CGM in youth with prediabetes and T2D. After adjusting for HbA1c, these alternate markers continued to predict components of glycemic variability detected by CGM.