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<p>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&#xa0;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 (<i>P</i> &lt; 0.001), but adjGA was not. HbA1c, 24-h urine protein(24hUP) and serum albumin (ALB) were influencing variables for mGA (<i>P</i> &lt; 0.001), while 24hUP and ALB had no effect on adjGA (<i>P</i> &gt; 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.</p>

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Adjusted glycated albumin is a novel indicator of glycemic control in patients with macroalbuminuria in diabetic kidney disease

  • 期刊论文

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  • 发表日期：

    2025年4月21日

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  • Jin Xie,
  • Ze-Hou Wang,
  • Zong-jin Zhang,
  • Yi-min Li,
  • Cun Shen,
  • Yuan Meng,
  • Wen-Jing Zhao,
  • Dan-Qian Chen,
  • Lu-Ying Sun,
  • Yue-Fen Wang

摘要

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.