Dataset Information

Circulating RNAs as predictive markers for the progression of type 2 diabetes.

ABSTRACT: Type 2 Diabetes Mellitus (T2DM) is the most prevalent form of diabetes in the USA, thus, the identification of biomarkers that could be used to predict the progression from prediabetes to T2DM would be greatly beneficial. Recently, circulating RNA including microRNAs (miRNAs) present in various body fluids have emerged as potential biomarkers for various health conditions, including T2DM. Whereas studies that examine the changes of miRNA spectra between healthy controls and T2DM individuals have been reported, the goal of this study is to conduct a baseline comparison of prediabetic individuals who either progress to T2DM, or remain prediabetic. Using an advanced small RNA sequencing library construction method that improves the detection of miRNA species, we identified 57 miRNAs that showed significant concentration differences between progressors (progress from prediabetes to T2DM) and non-progressors. Among them, 26 have been previously reported to be associated with T2DM in either body fluids or tissue samples. Some of the miRNAs identified were also affected by obesity. Furthermore, we identified miRNA panels that are able to discriminate progressors from non-progressors. These results suggest that upon further validation these miRNAs may be useful to predict the risk of conversion to T2DM from prediabetes.

SUBMITTER: Ghai V

PROVIDER: S-EPMC6433655 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Circulating RNAs as predictive markers for the progression of type 2 diabetes.

Ghai Vikas V Baxter David D Wu Xiaogang X Kim Taek-Kyun TK Kuusisto Johanna J Laakso Markku M Connolly Tom T Li Yong Y Andrade-Gordon Patricia P Wang Kai K

Journal of cellular and molecular medicine 20190207 4

Type 2 Diabetes Mellitus (T2DM) is the most prevalent form of diabetes in the USA, thus, the identification of biomarkers that could be used to predict the progression from prediabetes to T2DM would be greatly beneficial. Recently, circulating RNA including microRNAs (miRNAs) present in various body fluids have emerged as potential biomarkers for various health conditions, including T2DM. Whereas studies that examine the changes of miRNA spectra between healthy controls and T2DM individuals have ...[more]

PMID: 30734465

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Project description:BackgroundEffective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors.MethodsNuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.ResultsDuring median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]) and relative (15.0% [10.5-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).ConclusionsWhen added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.

Project description:Defining the role of T-cell avidity and killing efficacy in forming immunological response(s), leading to relapse-remission and autoantibody release in autoimmune type 1 diabetes (T1D), remains incompletely understood. Using competition-based population models of T- and B-cells, we provide a predictive tool to determine how these two parametric quantities, namely, avidity and killing efficacy, affect disease outcomes. We show that, in the presence of T-cell competition, successive waves along with cyclic fluctuations in the number of T-cells are exhibited by the model, with the former induced by transient bistability and the latter by transient periodic orbits. We hypothesize that these two immunological processes are responsible for making T1D a relapsing-remitting disease within prolonged but limited durations. The period and the number of peaks of these two processes differ, making them potential candidates to determine how plausible waves and cyclic fluctuations are in producing such effects. By assuming that T-cell and B-cell avidities are correlated, we demonstrate that autoantibodies associated with the higher avidity T-cell clones are first to be detected, and they reach their detectability level faster than those associated with the low avidity clones, independent of what T-cell killing efficacies are. Such outcomes are consistent with experimental observations in humans and they provide a rationale for observing rapid and slow progressors of T1D in high risk subjects. Our analysis of the models also reveals that it is possible to improve disease outcomes by unexpectedly increasing the avidity of certain subclones of T-cells. The decline in the number of β-cells in these cases still occurs, but it terminates early, leaving sufficient number of functioning β-cells in operation and the affected individual asymptomatic. These results indicate that the models presented here are of clinical relevance because of their potential use in developing predictive algorithms of rapid and slow progression to clinical T1D.

Dataset Information

Circulating RNAs as predictive markers for the progression of type 2 diabetes.

Publications

Circulating RNAs as predictive markers for the progression of type 2 diabetes.

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