Integrated modeling of labile and glycated hemoglobin with glucose for enhanced diabetes detection and short-term monitoring
Metabolic biomarkers, particularly glycated hemoglobin and fasting plasma glucose, are pivotal in the diagnosis and control of diabetes mellitus. Despite their importance, they exhibit limitations in assessing short-term glucose variations. In this study, we propose labile hemoglobin as an additiona...
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Veröffentlicht in: | iScience 2024-04, Vol.27 (4), p.109369-109369, Article 109369 |
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Sprache: | eng |
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Zusammenfassung: | Metabolic biomarkers, particularly glycated hemoglobin and fasting plasma glucose, are pivotal in the diagnosis and control of diabetes mellitus. Despite their importance, they exhibit limitations in assessing short-term glucose variations. In this study, we propose labile hemoglobin as an additional biomarker, providing insightful perspectives into these fluctuations. By utilizing datasets from 40,652 retrospective general participants and conducting glucose tolerance tests on 60 prospective pediatric subjects, we explored the relationship between plasma glucose and labile hemoglobin. A mathematical model was developed to encapsulate short-term glucose kinetics in the pediatric group. Applying dimensionality reduction techniques, we successfully identified participant subclusters, facilitating the differentiation between diabetic and non-diabetic individuals. Intriguingly, by integrating labile hemoglobin measurements with plasma glucose values, we were able to predict the likelihood of diabetes in pediatric subjects, underscoring the potential of labile hemoglobin as a significant glycemic biomarker for diabetes research.
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•Retrospective and prospective study of diabetic biomarkers in over 40,000 individuals•Dimensionality reduction of the large individual dataset reveals diabetic subclusters•Labile hemoglobin identifies diabetic patients undetected by glycated hemoglobin alone•A mathematical model captures the dynamics of glucose, labile, and glycated hemoglobins
Classification description: Biological sciences; Human metabolism; Mathematical biosciences; Structures |
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ISSN: | 2589-0042 2589-0042 |
DOI: | 10.1016/j.isci.2024.109369 |