Haemoglobin glycation index as an independent predictor of diabetic kidney disease in type 2 diabetes mellitus: a retrospective analysis.
The haemoglobin glycation index (HGI) reflects individual variations in glycation tendency and may offer additional value beyond HbA1c in predicting diabetes-related complications. This study aimed to evaluate the association and predictive value of HGI for diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).
A total of 400 T2DM patients were enrolled. Predicted HbA1c was calculated using a linear regression equation (R2=0.454) derived from fasting plasma glucose (FPG) and HGI was defined as the difference between measured and predicted HbA1c. Paired t-tests and Pearson correlation assessed the relationship between measured and predicted HbA1c. Multivariate logistic regression and receiver operating characteristic (ROC) analysis used to evaluate HGI as a predictor of DKD.
A strong positive correlation observed (r=0.674, p<0.001) between measured and predicted HbA1c and no significant difference observed (p=0.964) among the T2DM population. DKD was identified in 192 participants, who demonstrated significantly higher HGI compared to non-DKD patients (p=0.002). Multivariate analysis showed HGI (OR: 1.249, 95% CI: 1.053-1.482, p=0.011) and eGFR (OR: 0.964, 95% CI: 0.952-0.976, p<0.001) were independent risk factors for DKD. ROC analysis showed HGI as a moderate predictor of DKD (AUC=0.722, p<0.001), with an optimal cutoff of 0.53 carries 56.3% sensitivity and 81.2% specificity.
HGI is independently associated with DKD in T2DM and may serve as a useful adjunct marker, complimenting HbA1c and urinary albumin-to-creatinine ratio (UACR) for early identification of those at increased risk of kidney complications.
A total of 400 T2DM patients were enrolled. Predicted HbA1c was calculated using a linear regression equation (R2=0.454) derived from fasting plasma glucose (FPG) and HGI was defined as the difference between measured and predicted HbA1c. Paired t-tests and Pearson correlation assessed the relationship between measured and predicted HbA1c. Multivariate logistic regression and receiver operating characteristic (ROC) analysis used to evaluate HGI as a predictor of DKD.
A strong positive correlation observed (r=0.674, p<0.001) between measured and predicted HbA1c and no significant difference observed (p=0.964) among the T2DM population. DKD was identified in 192 participants, who demonstrated significantly higher HGI compared to non-DKD patients (p=0.002). Multivariate analysis showed HGI (OR: 1.249, 95% CI: 1.053-1.482, p=0.011) and eGFR (OR: 0.964, 95% CI: 0.952-0.976, p<0.001) were independent risk factors for DKD. ROC analysis showed HGI as a moderate predictor of DKD (AUC=0.722, p<0.001), with an optimal cutoff of 0.53 carries 56.3% sensitivity and 81.2% specificity.
HGI is independently associated with DKD in T2DM and may serve as a useful adjunct marker, complimenting HbA1c and urinary albumin-to-creatinine ratio (UACR) for early identification of those at increased risk of kidney complications.