Association between 1,5-Anhydroglucitol and Acute C Peptide Response to Arginine among Patients with Type 2 Diabetes

Shen, Yun; Si, Yiming; Lu, Jingyi; Ma, Xiaojing; Zhang, Lei; Mo, Yifei; Lü, Wei; Zhu, Wei; Bao, Yuqian; Hu, Gang; Zhou, Jian

doi:10.1155/2020/4243053

Cited by 9 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, 1,5-AG can be an effective supplementary marker to hemoglobin A1c. Low plasma levels of 1,5-AG are associated with decreased pancreatic β-cell function ( 33 ). Sorbitol takes part in the polyol pathway through the reduction of intracellular glucose to sorbitol.…”

Section: Discussionmentioning

confidence: 99%

Untargeted metabolomics reveals gender- and age- independent metabolic changes of type 1 diabetes in Chinese children

Zhang

Huang

et al. 2022

Front. Endocrinol.

View full text Add to dashboard Cite

IntroductionType 1 diabetes (T1D) is a chronic condition associated with multiple complications that substantially affect both the quality of life and the life-span of children. Untargeted Metabolomics has provided new insights into disease pathogenesis and risk assessment.MethodsIn this study, we characterized the serum metabolic profiles of 76 children with T1D and 65 gender- and age- matched healthy controls using gas chromatography coupled with timeof-flight mass spectrometry. In parallel, we comprehensively evaluated the clinical phenome of T1D patients, including routine blood and urine tests, and concentrations of cytokines, hormones, proteins, and trace elements.ResultsA total of 70 differential metabolites covering 11 metabolic pathways associated with T1D were identified, which were mainly carbohydrates, indoles, unsaturated fatty acids, amino acids, and organic acids. Subgroup analysis revealed that the metabolic changes were consistent among pediatric patients at different ages or gender but were closely associated with the duration of the disease.DiscussionCarbohydrate metabolism, unsaturated fatty acid biosynthesis, and gut microbial metabolism were identified as distinct metabolic features of pediatric T1D. These metabolic changes were also associated with T1D, which may provide important insights into the pathogenesis of the complications associated with diabetes.

show abstract

Section: Discussionmentioning

confidence: 99%

Untargeted metabolomics reveals gender- and age- independent metabolic changes of type 1 diabetes in Chinese children

Zhang

Huang

et al. 2022

Front. Endocrinol.

View full text Add to dashboard Cite

show abstract

“…Fasting C peptide (FCP), HbA1c, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol were determined as previously reported. 15 In addition to FCP, C peptide at 120 min (CP2h) during a standard meal following overnight fast was assayed. The increments of C peptide in plasma levels at 120 min (ΔCP2h=CP2h−FCP) showed the ratio of C peptide response.…”

Section: Methodsmentioning

confidence: 99%

Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes

Tao

Xia

et al. 2021

BMJ Open Diab Res Care

Self Cite

View full text Add to dashboard Cite

IntroductionMining knowledge from continuous glucose monitoring (CGM) data to classify highly heterogeneous patients with type 2 diabetes according to their characteristics remains unaddressed. A refined clustering method that retrieves hidden information from CGM data could provide a viable method to identify patients with different degrees of dysglycemia and clinical phenotypes.Research design and methodsFrom Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, we selected 908 patients with type 2 diabetes (18–83 years) who wore blinded CGM sensors (iPro2, Medtronic, California, USA). Participants were clustered based on CGM data during a 24-hour period by our method. The first level extracted the knowledge-based and statistics-based features to describe CGM signals from multiple perspectives. The Fisher score and variables cluster analysis were applied to fuse features into low dimensions at the second level. The third level divided subjects into subgroups with different clinical phenotypes. The four subgroups of patients were determined by clinical phenotypes.ResultsFour subgroups of patients with type 2 diabetes with significantly different statistical features and clinical phenotypes were identified by our method. In particular, individuals in cluster 1 were characterized by the lowest glucose level factor and glucose fluctuation factor, and the highest negative glucose factor and C peptide index. By contrast, cluster 2 had the highest glucose level factor and the lowest C peptide index. Cluster 4 was characterized by the greatest degree of glucose fluctuation factor, was the most insulin-sensitive, and had the lowest insulin resistance. Cluster 3 ranked in the middle concerning the CGM-derived metrics and clinical phenotypes compared with those of the other three groups.ConclusionA novel multilevel clustering approach for knowledge mining from CGM data in type 2 diabetes is presented. The results demonstrate that subgroups are adequately distinguished with notable statistical and clinical differences.

show abstract

“…Y Shen et al. ( 68 ) investigated the relationship between acute C peptide response to arginine and serum 1,5-AG in 623 T2DM patients, showing a linear relationship between the two, while acute C peptide response was an indicator of responsive β cell function, further illustrating that 1,5-AG was closely related to β cell function. H Su et al.…”

Section: Progress Of Clinical Research On 15-ag For Diabetesmentioning

confidence: 99%

The progress of clinical research on the detection of 1,5-anhydroglucitol in diabetes and its complications

Xu,

Pan,

Chen

2024

Front. Endocrinol.

View full text Add to dashboard Cite

1,5-Anhydroglucitol (1,5-AG) is sensitive to short-term glucose fluctuations and postprandial hyperglycemia, which has great potential in the clinical application of diabetes as a nontraditional blood glucose monitoring indicator. A large number of studies have found that 1,5-AG can be used to screen for diabetes, manage diabetes, and predict the perils of diabetes complications (diabetic nephropathy, diabetic cardiovascular disease, diabetic retinopathy, diabetic pregnancy complications, diabetic peripheral neuropathy, etc.). Additionally, 1,5-AG and β cells are also associated with each other. As a noninvasive blood glucose monitoring indicator, salivary 1,5-AG has much more benefit for clinical application; however, it cannot be ignored that its detection methods are not perfect. Thus, a considerable stack of research is still needed to establish an accurate and simple enzyme assay for the detection of salivary 1,5-AG. More clinical studies will also be required in the future to confirm the normal reference range of 1,5-AG and its role in diabetes complications to further enhance the blood glucose monitoring system for diabetes.

show abstract

Association between 1,5-Anhydroglucitol and Acute C Peptide Response to Arginine among Patients with Type 2 Diabetes

Cited by 9 publications

References 32 publications

Untargeted metabolomics reveals gender- and age- independent metabolic changes of type 1 diabetes in Chinese children

Untargeted metabolomics reveals gender- and age- independent metabolic changes of type 1 diabetes in Chinese children

Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes

The progress of clinical research on the detection of 1,5-anhydroglucitol in diabetes and its complications

Contact Info

Product

Resources

About