Postload Glycated Albumin as an Alternate Measure for Diabetes Screening in a Chinese Population

Establishment of a reference interval for glycated albumin based on medical check-up data from multiple medical institutions

Objectives: Data were collected to establish a reference interval for glycated albumin (GA), as well as to calculate a cutoff value for diagnosing diabetes mellitus and the GA level corresponding to a 75-g oral glucose tolerance test (OGTT) 2 h plasma glucose (2h-PG) level of 200 mg/dL.Methods: This study involved 1,843 subjects who were undergoing medical check-ups at several medical institutions and whose HbA1c and GA levels had been measured by OGTT.Results: The GA reference interval that was calculated based on the data obtained from study subjects with normal glucose tolerance was 12.1-17.1%. Using standardized major axis regression, the levels that corresponded to an OGTT 2h-PG level of 11.1 mmol/L were a GA level of 17.5% and an HbA1c level of 47.5 mmol/mol. A receiver-operating characteristic curve analysis was used to calculate the points at which sensitivity and specificity matched as the cutoff values, and the results yielded a GA level of 15.0% (sensitivity 69.3%).Conclusions: The GA reference interval was calculated to be 12.1-17.1%. We propose a GA level of 17.4% as a cutoff value to diagnose diabetes mellitus and a GA level of 15.0% as a screening cutoff value for diabetes mellitus, taking previous reports into account.

Glycated albumin in diabetes mellitus: a meta-analysis of diagnostic test accuracy

OBJECTIVES

Guidelines recommend the diagnosis of diabetes should be based on either plasma glucose or glycated hemoglobin (HbA_1C) findings. However, lately studies have advocated glycated albumin (GA) as a useful alternative to HbA_1c. We conducted a systematic review and meta-analysis to determine the overall diagnostic accuracy of GA for the diagnosis of diabetes.

CONTENT

We searched for articles of GA diabetes diagnostic accuracy that were published up to August 2021. Studies were selected if reported an oral glucose tolerance test as a reference test, measured GA levels by enzymatic methods, and had data necessary for 2 × 2 contingency tables. A bivariate model was used to calculate the pooled estimates.

SUMMARY

This meta-analysis included nine studies, totaling 10,007 individuals. Of those, 3,106 had diabetes. The studies showed substantial heterogeneity caused by a non-threshold effect and reported different GA optimal cut-offs for diagnosing diabetes. The pooled diagnostic odds ratio (DOR) was 15.93 and the area under the curve (AUC) was 0.844, indicating a good level of overall accuracy for the diagnosis of diabetes. The effect of the GA threshold on diagnostic accuracy was reported at 15.0% and 17.1%. The optimal cut-off for diagnosing diabetes with GA was estimated as 17.1% with a pooled sensitivity of 55.1% (95% CI 36.7%-72.2%) and specificity of 94.4% (95% CI 85.3%-97.9%).

OUTLOOK

GA has good diabetes diagnostic accuracy. A GA threshold of 17.1% may be considered optimal for diagnosing diabetes in previously undiagnosed individuals.

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.

Increasing waist circumference is associated with decreased levels of glycated albumin

BACKGROUND

Glycated albumin (GA) levels are affected by body fat and its distribution. We explored the association of waist circumference (WC) with GA and to assess the extent to which WC influences GA.

METHODS

We recruited 1799 subjects (age 26-82 y) from communities. GA was determined using the enzyme method, and glycated hemoglobin A_1c (HbA_1c) was detected using high-performance liquid chromatography.

RESULTS

Subjects with central obesity had lower GA and GA/HbA_1c than those without (both P < .01). GA and GA/HbA_1c were negatively correlated with central obesity (both P < .01), whereas HbA_1c was not correlated (P = .833). In the euglycemic and hyperglycemic subpopulations, GA and GA/HbA_1c showed decreasing trends as WC levels increased (both P for trends <0.01). WC was a significant negative determinant of GA (P < .05). In the hyperglycemic subpopulation, the GA value decreased by approximately 0.15% for each 5 cm increment in WC regardless of the presence of central obesity.

CONCLUSIONS

The GA value was reduced by approximately 0.15% for each 5 cm increment in WC, suggesting that more attention should be paid to actual blood glucose underestimated by GA in obese people.