Commentary on α-1-microglobulin as a predictor of diabetic complications

doi:10.4239/wjd.v17.i1.114210

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Jan 15, 2026 (publication date) through Feb 28, 2026

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World Journal of Diabetes

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Letter to the Editor

World J Diabetes. Jan 15, 2026; 17(1): 114210
Published online Jan 15, 2026. doi: 10.4239/wjd.v17.i1.114210

Commentary on α-1-microglobulin as a predictor of diabetic complications

Li Liao, An Luo, Long-Han Zhang, Yi-Ting Pan, Ya-Qing Liu

Li Liao, An Luo, Long-Han Zhang, Yi-Ting Pan, Ya-Qing Liu, School of Nursing, University of South China, Hengyang 421001, Hunan Province, China

Author contributions: Liao L and Luo A conceived the study and drafted the manuscript; Zhang LH, Pan YT and Liu YQ contributed to critical review, editing, and intellectual revision of the content; all authors have read and approved the final manuscript.

Conflict-of-interest statement: The authors declare that they have no conflicts of interest related to this manuscript.

Corresponding author: Li Liao, PhD, School of Nursing, University of South China, No. 28 West Changsheng Road, Hengyang 421001, Hunan Province, China. 254251558@qq.com

Received: September 15, 2025
Revised: October 25, 2025
Accepted: November 19, 2025
Published online: January 15, 2026
Processing time: 122 Days and 23 Hours

Abstract

This letter comments on the study by Ge et al. Serum α1-microglobulin (α1-MG) was reported to be associated with diabetic nephropathy, retinopathy, peripheral neuropathy, and left ventricular hypertrophy. The letter further highlights the need for multicenter validation and evaluation of clinical applicability to clarify the role of α1-MG in diabetes management.

Keywords: α1-microglobulin; Type 2 diabetes mellitus; Machine learning models; Commentary

Core Tip: This letter comments on the recent study regarding serum α1-microglobulin (α1-MG) as a predictor of multiple complications in type 2 diabetes mellitus. The letter underscores the importance of α1-MG as a potential biomarker linking renal, retinal, neural, and cardiac complications, and highlights the need for multi-center validation and cost-effectiveness evaluation before clinical adoption.