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Risk of type 1 diabetes progression in islet autoantibody-positive children can be further stratified using expression patterns of multiple genes implicated in peripheral blood lymphocyte activation and function.

Jin Y, Sharma A, Bai S, Davis C, Liu H, Hopkins D, Barriga K, Rewers M, She JX - Diabetes (2014)

Bottom Line: RT-PCR analyses of the top-27 candidate genes confirmed 5 genes (BACH2, IGLL3, EIF3A, CDC20, and TXNDC5) associated with differential progression and implicated in lymphocyte activation and function.Multivariate analyses of these five genes in the discovery and validation data sets identified and confirmed four multigene models (BI, ICE, BICE, and BITE, with each letter representing a gene) that consistently stratify high- and low-risk subsets of AbP subjects with hazard ratios >6 (P < 0.01).The results suggest that these genes may be involved in T1D pathogenesis and potentially serve as excellent gene expression biomarkers to predict the risk of progression to clinical diabetes for AbP subjects.

View Article: PubMed Central - PubMed

Affiliation: Sino-American Institute of Translational Medicine, School of Pharmaceutical Sciences, Nanjing University of Technology, Nanjing, ChinaCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Georgia Regents University, Augusta, GADepartment of Pathology, Medical College of Georgia, Georgia Regents University, Augusta, GA.

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Risk stratification consistency in individual subjects by model. Model 1, BI; model 2, ICE; model 3, BICE; and model 4, BITE. M, microarray data set; P, real-time RT-PCR data set.
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Figure 3: Risk stratification consistency in individual subjects by model. Model 1, BI; model 2, ICE; model 3, BICE; and model 4, BITE. M, microarray data set; P, real-time RT-PCR data set.

Mentions: Having shown the utility of the multigene models in risk stratification of AbP subjects, we next assessed the consistency of risk stratification for individual subjects using the various models. Fig. 3 shows the consistency among all possible combinations of these top-four models. A major conclusion from these results is that 75% of the individuals are consistently classified into the same progression group by all four models. Further examination of the data suggests that model ICE has less consistency than the other three models (BI, BICE, and BITE). The mean consistency among models excluding ICE is >89% for both data sets, whereas the mean consistencies for models including ICE are 82.6% and 84.4% for the discovery and validation data sets, respectively. Indeed, ICE has the lowest HR and highest AIC in both the discovery and the validation data sets compared with the other three models (Fig. 2, Table 3).


Risk of type 1 diabetes progression in islet autoantibody-positive children can be further stratified using expression patterns of multiple genes implicated in peripheral blood lymphocyte activation and function.

Jin Y, Sharma A, Bai S, Davis C, Liu H, Hopkins D, Barriga K, Rewers M, She JX - Diabetes (2014)

Risk stratification consistency in individual subjects by model. Model 1, BI; model 2, ICE; model 3, BICE; and model 4, BITE. M, microarray data set; P, real-time RT-PCR data set.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4066338&req=5

Figure 3: Risk stratification consistency in individual subjects by model. Model 1, BI; model 2, ICE; model 3, BICE; and model 4, BITE. M, microarray data set; P, real-time RT-PCR data set.
Mentions: Having shown the utility of the multigene models in risk stratification of AbP subjects, we next assessed the consistency of risk stratification for individual subjects using the various models. Fig. 3 shows the consistency among all possible combinations of these top-four models. A major conclusion from these results is that 75% of the individuals are consistently classified into the same progression group by all four models. Further examination of the data suggests that model ICE has less consistency than the other three models (BI, BICE, and BITE). The mean consistency among models excluding ICE is >89% for both data sets, whereas the mean consistencies for models including ICE are 82.6% and 84.4% for the discovery and validation data sets, respectively. Indeed, ICE has the lowest HR and highest AIC in both the discovery and the validation data sets compared with the other three models (Fig. 2, Table 3).

Bottom Line: RT-PCR analyses of the top-27 candidate genes confirmed 5 genes (BACH2, IGLL3, EIF3A, CDC20, and TXNDC5) associated with differential progression and implicated in lymphocyte activation and function.Multivariate analyses of these five genes in the discovery and validation data sets identified and confirmed four multigene models (BI, ICE, BICE, and BITE, with each letter representing a gene) that consistently stratify high- and low-risk subsets of AbP subjects with hazard ratios >6 (P < 0.01).The results suggest that these genes may be involved in T1D pathogenesis and potentially serve as excellent gene expression biomarkers to predict the risk of progression to clinical diabetes for AbP subjects.

View Article: PubMed Central - PubMed

Affiliation: Sino-American Institute of Translational Medicine, School of Pharmaceutical Sciences, Nanjing University of Technology, Nanjing, ChinaCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Georgia Regents University, Augusta, GADepartment of Pathology, Medical College of Georgia, Georgia Regents University, Augusta, GA.

Show MeSH
Related in: MedlinePlus