TLDR Hair analysis can effectively detect diabetes and aging markers.
This study demonstrates that hair analysis is an effective non-invasive method for assessing metabolic fluctuations related to diabetes and aging. Using mass spectrometry, researchers detected advanced glycation end products (AGEs) in hair samples from humans and rats, finding that levels of AGEs such as Nε-(carboxymethyl)lysine (CML) and methylglyoxal-derived hydroimidazolone-1 (MG-H1) remained stable despite temperature variations. In diabetic model rats, hair CEL and MG-H1 levels were significantly higher than in non-diabetic rats, with a perfect area under the curve of 1 in receiver operating characteristic analysis. The study found positive correlations between age and CML levels, as well as between serum and hair CML levels, suggesting that hair can serve as a reliable marker for long-term metabolic evaluation.
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