AIMS: Glycated albumin (GA) is a biomarker for short-term (2-3 weeks) glycaemic control. However, the predictive utility of GA for diabetes and prediabetes is largely uncharacterised. We aimed to investigate the relationships of baseline serum GA levels with incident diabetes and prediabetes. METHODS: This was a longitudinal cohort study involving 516 subjects without diabetes or prediabetes at baseline. Blood glucose levels were observed during follow-up. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using COX proportional hazard models. Receiver operating characteristic curves and areas under the curves (AUCs) were used to evaluate the discriminating abilities of glycaemic biomarkers and prediction models. RESULTS: During a 9-year follow-up, 51 individuals (9.88%) developed diabetes and 92 (17.83%) prediabetes. Unadjusted HRs (95% CI) for both diabetes and prediabetes increased proportionally with increasing GA levels in a dose-response manner. Multivariable-adjusted HRs (95% CI) for diabetes were significantly elevated from 1.0 (reference) to 5.58 (1.86-16.74). However, the trend was no longer significant for prediabetes after multivariable adjustment. AUCs for GA, fasting blood glucose (FBG) and 2-h postprandial blood glucose (2h-PBG) for predicting diabetes were 0.698, 0.655 and 0.725, respectively. The AUCs for GA had no significant differences compared with those for FBG (p = 0.376) and 2h-PBG (p = 0.552). Replacing FBG or 2h-PBG or both with GA in diabetes prediction models made no significant changes to the AUCs of the models. CONCLUSIONS: GA is of good prognostic utility in predicting diabetes. However, GA may not be a useful biomarker for predicting prediabetes.
AIMS: Glycated albumin (GA) is a biomarker for short-term (2-3 weeks) glycaemic control. However, the predictive utility of GA for diabetes and prediabetes is largely uncharacterised. We aimed to investigate the relationships of baseline serum GA levels with incident diabetes and prediabetes. METHODS: This was a longitudinal cohort study involving 516 subjects without diabetes or prediabetes at baseline. Blood glucose levels were observed during follow-up. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using COX proportional hazard models. Receiver operating characteristic curves and areas under the curves (AUCs) were used to evaluate the discriminating abilities of glycaemic biomarkers and prediction models. RESULTS: During a 9-year follow-up, 51 individuals (9.88%) developed diabetes and 92 (17.83%) prediabetes. Unadjusted HRs (95% CI) for both diabetes and prediabetes increased proportionally with increasing GA levels in a dose-response manner. Multivariable-adjusted HRs (95% CI) for diabetes were significantly elevated from 1.0 (reference) to 5.58 (1.86-16.74). However, the trend was no longer significant for prediabetes after multivariable adjustment. AUCs for GA, fasting blood glucose (FBG) and 2-h postprandial blood glucose (2h-PBG) for predicting diabetes were 0.698, 0.655 and 0.725, respectively. The AUCs for GA had no significant differences compared with those for FBG (p = 0.376) and 2h-PBG (p = 0.552). Replacing FBG or 2h-PBG or both with GA in diabetes prediction models made no significant changes to the AUCs of the models. CONCLUSIONS: GA is of good prognostic utility in predicting diabetes. However, GA may not be a useful biomarker for predicting prediabetes.
Authors: Andrew Yen Siong Tan; Mui Suan Tan; Ashley Wu; Ai Choo Seah; Cecilia Chong; Eileen Koh; Ngiap Chuan Tan Journal: BMJ Open Diabetes Res Care Date: 2021-12