Background PCSK9 gene expression is associated with biological processes such as lipid metabolism, glucose metabolism, and inflammation. In the present study, our primary objective was to assess the association between the single-nucleotide polymorphisms in the PCSK9 gene and type 2 diabetes in Uygur subjects, in Xinjiang, China. Methods We designed a case–control study including 662 patients diagnosed with T2DM and 1220 control subjects. Four single-nucleotide polymorphisms (rs11583680, rs2483205, rs2495477 and rs562556) of PCSK9 gene were genotyped using the improved multiplex ligation detection reaction technique. Results For rs2483205, the distribution of genotypes, dominant model (CC vs CT + TT), overdominant model (CC + TT vs CT) showed significant differences between T2DM patients and the controls (P = 0.011 and P = 0.041 respectively). For rs2495477, the distribution of genotypes, the dominant model (AA vs GA + GG) showed significant differences between T2DM patients and the controls (P = 0.024). Logistic regression analysis suggested after adjustment of other confounders, the differences remained significant between the two groups [for rs2483205 CC vs CT + TT: odds ratio (OR) = 1.321, 95% confidence interval (CI) 1.078–1.617, P = 0.007; CC + TT vs CT: OR = 1.255, 95% CI 1.021–1.542, P = 0.03; for rs2495477 AA vs GA + GG: OR = 1.297, 95% CI 1.060–1.588, P = 0.012]. Conclusion The present study indicated that CT + TT genotype and CT genotype of rs2483205, as well as GA + GG genotype of rs2495477 in PCSK9 gene were associated with an increased risk of type 2 diabetes in the Uygur population in Xinjiang.
Purpose: we aimed to identify potential candidate biomarkers in aorta tissue from AAD patients. Methods: We used 4D label-free quantification (4D-LFQ) mass spectrometry to screen differentially expressed proteins in aorta tissues of AAD patients. Then we performed protein annotation, unsupervised hierarchical clustering, functional classification, functional enrichment and cluster, and protein-protein interaction analyses. Parallel Reaction Monitoring (PRM) technology was used to accurately and quantitatively confirm the selected target proteins. Results: A total of 3350 proteins were identified. Taking 1.5 times as the differential expression threshold, 139 were upregulated and 108 were downregulated as compared to the control groups. Bioinformatics analysis showed that the differential proteins were mainly distributed in extracellular matrix and cytoplasm. And their functions mainly involve cell migration and proliferation, inflammatory cell activation, cell contraction, muscle organ development and other processes. PRM technology accurately quantified the selected 20 target proteins, and found SAA1, LBP, MPO, and ENG were confirmed to be enriched in the aorta tissue of AAD patients. Conclusions: This is the first application of a 4D-LFQ-PRM workflow to identify and validate biomarkers in AAD patients. SAA1, LBP, MPO, and ENG represent novel biomarkers for the pathogenesis of AAD and might be a therapeutic target in the future.
There are few biomarkers of glycemic response among youth with T2D, despite increasing disease prevalence and suboptimal response to approved therapies. We hypothesized that plasma metabolites may predict glycemic outcomes in youth-onset T2D. We measured 480 metabolites in fasting plasma samples in the Treatment options for T2D in Adolescents and Youth (TODAY) study (n=391), in which youth with T2D aged 10-17 years were randomized to metformin alone, metformin + rosiglitazone, or metformin + intensive lifestyle intervention. Metabolite associations with loss of glycemic control (defined as HbA1c ≥8% for 6 months or need for insulin therapy) were modeled using Cox proportional hazards regression adjusted for baseline age, sex, race/ethnicity, BMI, treatment group, and fasting glucose. Loss of glycemic control was observed in 150 of 391 youth (mean 2.6 years). Baseline levels of 11 metabolites were associated with loss of glycemic control (FDR<0.05, Fig. 1A). Treatment group modified the association of hexose and xanthurenic acid levels with glycemic control. For both compounds, youth with higher baseline levels had a lower risk of treatment failure when randomized to metformin therapy alone (Fig. 1B). Thus, metabolomics provides insight into circulating analytes associated with loss of glycemic control, and may highlight different effects of specific treatments in youth with T2D. Disclosure Z. Chen: None. C. Lu: None. X. Shi: None. S. Zheng: None. D. Wolfs: None. P. Bjornstad: Advisory Panel; AstraZeneca, Novo Nordisk, Lilly, Horizon Therapeutics plc, Boehringer Ingelheim (Canada) Ltd., LG Chem, Consultant; Bayer Inc., Bristol-Myers Squibb Company. R. E. Gerszten: None. E. M. Isganaitis: None. Funding National Institutes of Health (K23DK127073)
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