Guideline for the Detection of Advanced Glycation End Products

ABSTRACT Advanced glycation end products (AGEs), stable products of nonenzymatic reactions between reducing sugars and biological macromolecules, are widely present in biological tissues and processed foods. Their aberrant accumulation links to chronic disorders such as diabetes mellitus and cardiovascular diseases. This experimental protocol focuses on systematic AGE detection methods to provide standardized operational references for relevant studies. Based on AGE's three classifications (cross‐linked fluorescent, non‐cross‐linked, and nonfluorescent cross‐linked) and dicarbonyl metabolite characteristics (e.g., MGO, GO), the guideline establishes a multitiered detection system: total AGEs quantified via fluorescence spectrophotometry, non‐cross‐linked AGEs (e.g., CML, CEL) specifically detected by HPLC–MS/MS, pentosidine targeted via LC–MS/MS, and reactive dicarbonyl intermediates (MGO, GO) determined by GC–MS. This furnishes a technically feasible and accurate framework for fundamental AGE research. This integrated approach enhances methodological standardization, reproducibility, and accuracy/comprehensiveness of AGE detection across samples. By providing clear workflows, method comparisons, and applicability guidelines, this document seeks reliable and comparable results in basic/applied AGE research.

Authors

Institutions

Publication Details

Journal
Food Safety and Health
Published
2025-09-23
DOI
https://doi.org/10.1002/fsh3.70045
Primary Topic
Advanced Glycation End Products research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Guideline for the Detection of Advanced Glycation End Products

Qian Wu, Boqian He, Chunlin Li, Yan Jia et al.
Food Safety and Health
Advanced Glycation End Products research
article

Guideline for the Detection of Advanced Glycation End Products

Qian Wu, Boqian He, Chunlin Li, Yan Jia, Jingying Liu, Siyu Han, Chenxu Bao
article en

Abstract

ABSTRACT Advanced glycation end products (AGEs), stable products of nonenzymatic reactions between reducing sugars and biological macromolecules, are widely present in biological tissues and processed foods. Their aberrant accumulation links to chronic disorders such as diabetes mellitus and cardiovascular diseases. This experimental protocol focuses on systematic AGE detection methods to provide standardized operational references for relevant studies. Based on AGE's three classifications (cross‐linked fluorescent, non‐cross‐linked, and nonfluorescent cross‐linked) and dicarbonyl metabolite characteristics (e.g., MGO, GO), the guideline establishes a multitiered detection system: total AGEs quantified via fluorescence spectrophotometry, non‐cross‐linked AGEs (e.g., CML, CEL) specifically detected by HPLC–MS/MS, pentosidine targeted via LC–MS/MS, and reactive dicarbonyl intermediates (MGO, GO) determined by GC–MS. This furnishes a technically feasible and accurate framework for fundamental AGE research. This integrated approach enhances methodological standardization, reproducibility, and accuracy/comprehensiveness of AGE detection across samples. By providing clear workflows, method comparisons, and applicability guidelines, this document seeks reliable and comparable results in basic/applied AGE research.

Food Safety and HealthVol. 4(1)
ZheJiang Academy of Agricultural Sciences (CN), Hubei University of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hubei Province
Openalex Percentile: Top 71%
Advanced Glycation End Products research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.