Integrated Biomarker Profiling of the Metabolome Associated With Impaired Fasting Glucose and Type 2 Diabetes Mellitus in a Large-Scale Chinese Patients

Long, Jianglan; Yang, Hui; Yang, Zhao; Jia, Qingquan; Liu, Liwei; Cui, Huijing; Ding, Shuzhe; Qin, Qian; Xing-zhong, Feng; Yan, Shuxun; Tang, Jinfa; Han, Ying; Qi, Ningning; Deng, Kejun; Lin, Hao; Sun, Zhi; Yan, Dan

doi:10.2139/ssrn.3759693

Cited by 4 publications

(5 citation statements)

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“…A large‐scale study of Chinese patients involving integrated biomarker profiling was conducted in order to investigate diagnostic biomarkers of prediabetes and of the metabolome in T2DM. Dysregulation of l ‐phenylalanine was associated with impaired fasting glucose and T2DM 37 . In this study, we found a remarkable increase in serum level of l ‐phenylalanine but a downregulation of l ‐phenylalanine in the brain.…”

Section: Discussionsupporting

confidence: 55%

“…Dysregulation of l-phenylalanine was associated with impaired fasting glucose and T2DM. 37 In this study, we found a remarkable increase in serum level of l-phenylalanine F I G U R E 8 Representative metabolites including glycine, l-glutamic acid, and l-aspartic acid were quantified using an amino acid analyzer. (A-C), The serum levels of glycine, l-glutamic acid, and l-aspartic acid were up-regulated after high fructose intake.…”

Section: Discussionmentioning

confidence: 72%

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Impact of a long‐term high‐fructose diet on systemic metabolic profiles of mice

Wang

Han

et al. 2022

FASEB BioAdvances

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Evidence is mounting that chronic high‐fructose diets (HFrD) can lead to metabolic abnormalities and cause a variety of diseases. However, the underlying mechanism by which long‐term high fructose intake influencing systemic metabolism remains unclarified. This study, therefore, attempted to investigate the impact of a high‐fructose diet on metabolic profile. Four‐week‐old male C57BL/6 mice were fed with 15% fructose solution as their only source of water for 8 weeks. Afterward, gas chromatography–mass spectrometry (GC–MS) was employed to investigate the comprehensive metabolic profile of serum, muscle, liver, heart, white adipose, brain, and kidney tissues, and multivariate analyses including principal component analysis (PCA) and orthogonal partial least squared‐discriminant analysis (OPLS‐DA) were applied to screen for differential metabolite expression between the HFrD and control groups. Furthermore, the MetaboAnalyst 5.0 ( http://www.metaboanalyst.ca ) and Kyoto Encyclopedia of Genes and Genomes database (KEGG; http://www.kegg.jp ) were employed to portray a detailed metabolic network. This study identified 62 metabolites related to HFrD and 10 disturbed metabolic pathways. The results indicated that high fructose intake mainly influenced amino acid metabolism and biosynthesis (glycine, serine, and threonine metabolism; aspartate, and glutamate metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis, and arginine biosynthesis pathways), glutathione metabolism, sphingolipid metabolism, and glyoxylate and dicarboxylate metabolism in serum, whereas these pathways were suppressed in the brain. Starch and sucrose metabolism in muscle was also disrupted. These results elucidate the effects of long‐term high fructose consumption on the metabolic profiles of various tissues and provide new insight for the identification of potential metabolic biomarkers and pathways disrupted by high fructose.

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Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 72%

Impact of a long‐term high‐fructose diet on systemic metabolic profiles of mice

Wang

Han

et al. 2022

FASEB BioAdvances

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“…Generally, features contribute unequally to the prediction model. Some features make key contributions, some make minor contributions, and some might even reduce the performance of the model [55] , [56] , [57] , [58] , [59] . Therefore, feature selection is a vital step to improve classification performance.…”

Section: Methodsmentioning

confidence: 99%

IBPred: A sequence-based predictor for identifying ion binding protein in phage

Yuan

Gao

Xie

et al. 2022

Computational and Structural Biotechnology Journal

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“…Feature Encoding. Generally, feature encoding plays a crucial role for machine learning in model construction [22][23][24][25][26][27][28]. The feature encoding method determines the degree of sequence information mining.…”

Section: Benchmarkmentioning

confidence: 99%

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features

Liu

Chen

Gao

et al. 2022

Computational and Mathematical Methods in Medicine

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Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.

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Integrated Biomarker Profiling of the Metabolome Associated With Impaired Fasting Glucose and Type 2 Diabetes Mellitus in a Large-Scale Chinese Patients

Cited by 4 publications

References 0 publications

Impact of a long‐term high‐fructose diet on systemic metabolic profiles of mice

Impact of a long‐term high‐fructose diet on systemic metabolic profiles of mice

IBPred: A sequence-based predictor for identifying ion binding protein in phage

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features

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