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A unique plasma microRNA profile defines type 2 diabetes progression

de Candia, Paola, Spinetti, Gaia, Specchia, Claudia, Sangalli, Elena, La Sala, Lucia, Uccellatore, Annachiara, Lupini, Silvia, Genovese, Stefano, Matarese, Giuseppe, Ceriello, Antonio·2017·PLOS ONE
DOI 10.1371/journal.pone.0188980PMC PMC5714331
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Fig 1 Dysregulation of plasma circulating miRNAs in glucose tolerance impaired subjects. (A) Boxplots (10–90 percentile) for the number of detected miRNAs (left) and miRNA Ct values (right) in the three indicated sample groups. Non-parametric Kruskal-Wallis p-values are reported at the bottom of the graph, while Dunn’s multiple comparison test (versus NGT group) p-values are reported on single group plots (* <0.05; ** <0.01; *** <0.001). (B) Scatter plots showing the correlation between miRNA global means (for the 27 individuals belonging to the three groups of NGT, IGT and T2D) expressed as reversed Ct values and either oral glucose tolerance test (OGTT, that refers to glucose level measured 2 hours post load, left) or glycated hemoglobin (HbA1c, right). Spearman correlation coefficient r and p-values are also reported. (C) Boxplot for single miRNA non-parametric correlation with the global mean values, averaged per group. Non-parametric Kruskal-Wallis p-value is reported at the bottom of the graph. Dunn’s multiple comparison test (versus IGT group) p-values are shown (* <0.05; *** <0.001). (D) Heatmap reporting the correlation index map (non parametric Spearman r values) of each co-expressed miRNA versus all the others. The map is divided per group as indicated. (E) Vertical scatter plots for Log10 transformed global mean normalized miRNA values in the three groups as indicated. The IGT group is divided in two further groups: progressors (P IGT) and non-progressors (NP IGT), based on their clinical history (see text). miRNAs were found differentially expressed in at least one group comparisons (t Test p-value<0.05, see Table 2). (F) Hierarchical clustering of the four groups (the IGT group being divided as in panel E) using the Log10 transformed normalized values of differentially expressed miRNAs (as for panel E).
Fig 1 Dysregulation of plasma circulating miRNAs in glucose tolerance impaired subjects. (A) Boxplots (10–90 percentile) for the number of detected miRNAs (left) and miRNA Ct values (right) in the three indicated sample groups. Non-parametric Kruskal-Wallis p-values are reported at the bottom of the graph, while Dunn’s multiple comparison test (versus NGT group) p-values are reported on single group plots (* <0.05; ** <0.01; *** <0.001). (B) Scatter plots showing the correlation between miRNA global means (for the 27 individuals belonging to the three groups of NGT, IGT and T2D) expressed as reversed Ct values and either oral glucose tolerance test (OGTT, that refers to glucose level measured 2 hours post load, left) or glycated hemoglobin (HbA1c, right). Spearman correlation coefficient r and p-values are also reported. (C) Boxplot for single miRNA non-parametric correlation with the global mean values, averaged per group. Non-parametric Kruskal-Wallis p-value is reported at the bottom of the graph. Dunn’s multiple comparison test (versus IGT group) p-values are shown (* <0.05; *** <0.001). (D) Heatmap reporting the correlation index map (non parametric Spearman r values) of each co-expressed miRNA versus all the others. The map is divided per group as indicated. (E) Vertical scatter plots for Log10 transformed global mean normalized miRNA values in the three groups as indicated. The IGT group is divided in two further groups: progressors (P IGT) and non-progressors (NP IGT), based on their clinical history (see text). miRNAs were found differentially expressed in at least one group comparisons (t Test p-value<0.05, see Table 2). (F) Hierarchical clustering of the four groups (the IGT group being divided as in panel E) using the Log10 transformed normalized values of differentially expressed miRNAs (as for panel E).CC BY · PubMed Central
Fig 2 Analysis of miRNA correlation with metabolic parameters. (A) Heatmap reporting the correlation index map (Spearman r values, red if positive, blue if negative) of differentially expressed miRNAs and the reported clinical parameters. BMI = body mass index; OGTT, 2h oral glucose tolerance test, at 2 hours; HOMA = homeostatic model assessment; HbA1c = glycated hemoglobin; LDL = low density lipoproteins; HDL = high density lipoproteins. Only miRNAs significantly correlated with at least two clinical parameters are shown. * p-values≤0.05. (B) Scatter plots showing the correlation between BMI (upper), waist (middle) and OGTT (lower) and miR-27a-3p normalized relative quantities (NRQs) for all 27 subjects under study. Spearman r and p-values are reported. (C) Scatter plots showing the correlation between miR-27a-3p Ct values and miRNA global mean for all 27 subjects divided into the three groups of NGT, IGT and T2D as indicated. Slope values are also reported.
Fig 2 Analysis of miRNA correlation with metabolic parameters. (A) Heatmap reporting the correlation index map (Spearman r values, red if positive, blue if negative) of differentially expressed miRNAs and the reported clinical parameters. BMI = body mass index; OGTT, 2h oral glucose tolerance test, at 2 hours; HOMA = homeostatic model assessment; HbA1c = glycated hemoglobin; LDL = low density lipoproteins; HDL = high density lipoproteins. Only miRNAs significantly correlated with at least two clinical parameters are shown. * p-values≤0.05. (B) Scatter plots showing the correlation between BMI (upper), waist (middle) and OGTT (lower) and miR-27a-3p normalized relative quantities (NRQs) for all 27 subjects under study. Spearman r and p-values are reported. (C) Scatter plots showing the correlation between miR-27a-3p Ct values and miRNA global mean for all 27 subjects divided into the three groups of NGT, IGT and T2D as indicated. Slope values are also reported.CC BY · PubMed Central

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Research works printed but not linked: 36

  1. J Lindstrom, 2003, The diabetes risk score: a practical tool to predict type 2 diabetes risk, Diabetes Care, 26, 725, 10.2337/diacare.26.3.725 · DOI
  2. M Franciosi, 2005, Use of the diabetes risk score for opportunistic screening of undiagnosed diabetes and impaired glucose tolerance: the IGLOO (Impaired Glucose Tolerance and Long-Term Outcomes Observational) study, Diabetes Care, 28, 1187, 10.2337/diacare.28.5.1187 · DOI · Paper page
  3. JD Maynard, 2007, Noninvasive type 2 diabetes screening: superior sensitivity to fasting plasma glucose and A1C, Diabetes Care, 30, 1120, 10.2337/dc06-2377 · DOI
  4. G Reid, 2011, Circulating microRNAs: Association with disease and potential use as biomarkers, Crit Rev Oncol Hematol, 80, 193, 10.1016/j.critrevonc.2010.11.004 · DOI
  5. M Boeri, 2011, MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer, Proceedings of the National Academy of Sciences of the United States of America, 108, 3713, 10.1073/pnas.1100048108 · DOI · Paper page
  6. E Moussay, 2011, MicroRNA as biomarkers and regulators in B-cell chronic lymphocytic leukemia, Proceedings of the National Academy of Sciences of the United States of America, 108, 6573, 10.1073/pnas.1019557108 · DOI
  7. DD Taylor, 2008, MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer, Gynecol Oncol, 110, 13, 10.1016/j.ygyno.2008.04.033 · DOI
  8. Y D'Alessandra, 2010, Circulating microRNAs are new and sensitive biomarkers of myocardial infarction, Eur Heart J, 31, 2765, 10.1093/eurheartj/ehq167 · DOI
  9. E Goretti, 2013, Association between circulating microRNAs, cardiovascular risk factors and outcome in patients with acute myocardial infarction, International journal of cardiology, 168, 4548, 10.1016/j.ijcard.2013.06.092 · DOI
  10. PJ Starkey Lewis, 2011, Circulating microRNAs as potential markers of human drug-induced liver injury, Hepatology, 54, 1767, 10.1002/hep.24538 · DOI
  11. YJ Chen, 2013, Circulating microRNAs as a Fingerprint for Liver Cirrhosis, PloS one, 8, e66577, 10.1371/journal.pone.0066577 · DOI
  12. JF Wang, 2010, Serum miR-146a and miR-223 as potential new biomarkers for sepsis, Biochem Biophys Res Commun, 394, 184, 10.1016/j.bbrc.2010.02.145 · DOI
  13. C Roderburg, 2013, Circulating microRNA-150 serum levels predict survival in patients with critical illness and sepsis, PloS one, 8, e54612, 10.1371/journal.pone.0054612 · DOI
  14. J Wang, 2016, MicroRNA as Biomarkers and Diagnostics, Journal of cellular physiology, 231, 25, 10.1002/jcp.25056 · DOI · Paper page
  15. G Sebastiani, 2017, Circulating microRNAs and diabetes mellitus: a novel tool for disease prediction, diagnosis, and staging?, J Endocrinol Invest
  16. AA Seyhan, 2016, Pancreas-enriched miRNAs are altered in the circulation of subjects with diabetes: a pilot cross-sectional study, Sci Rep, 6, 31479, 10.1038/srep31479 · DOI
  17. L Kong, 2011, Significance of serum microRNAs in pre-diabetes and newly diagnosed type 2 diabetes: a clinical study, Acta Diabetol, 48, 61, 10.1007/s00592-010-0226-0 · DOI
  18. Y Liu, 2014, The role of circulating microRNA-126 (miR-126): a novel biomarker for screening prediabetes and newly diagnosed type 2 diabetes mellitus, Int J Mol Sci, 15, 10567, 10.3390/ijms150610567 · DOI · Paper page
  19. P Prabu, 2015, Circulating MiRNAs of 'Asian Indian Phenotype' Identified in Subjects with Impaired Glucose Tolerance and Patients with Type 2 Diabetes, PloS one, 10, e0128372, 10.1371/journal.pone.0128372 · DOI
  20. M Parrizas, 2015, Circulating miR-192 and miR-193b are markers of prediabetes and are modulated by an exercise intervention, J Clin Endocrinol Metab, 100, E407, 10.1210/jc.2014-2574 · DOI
  21. M Kalis, 2011, Beta-cell specific deletion of Dicer1 leads to defective insulin secretion and diabetes mellitus, PloS one, 6, e29166, 10.1371/journal.pone.0029166 · DOI
  22. V Rottiers, 2012, MicroRNAs in metabolism and metabolic disorders, Nat Rev Mol Cell Biol, 13, 239, 10.1038/nrm3313 · DOI · Paper page
  23. AS Meijnikman, 2016, Screening for type 2 diabetes mellitus in overweight and obese subjects made easy by the FINDRISC score, J Diabetes Complications, 30, 1043, 10.1016/j.jdiacomp.2016.05.004 · DOI
  24. A American Diabetes, 2017, Standards of Medical Care in Diabetes-2017 Abridged for Primary Care Providers, Clin Diabetes, 35, 5, 10.2337/cd16-0067 · DOI
  25. CL Andersen, 2004, Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets, Cancer Res, 64, 5245, 10.1158/0008-5472.CAN-04-0496 · DOI · Paper page
  26. A Ceriello, 2010, 'Glycaemic variability': a new therapeutic challenge in diabetes and the critical care setting, Diabet Med, 27, 862, 10.1111/j.1464-5491.2010.02967.x · DOI
  27. A Ceriello, 2013, Glycemic variability: both sides of the story, Diabetes Care, 36, S272, 10.2337/dcS13-2030 · DOI · Paper page
  28. L La Sala, 2016, Oscillating glucose induces microRNA-185 and impairs an efficient antioxidant response in human endothelial cells, Cardiovasc Diabetol, 15, 71, 10.1186/s12933-016-0390-9 · DOI
  29. X Tang, 2009, Identification of glucose-regulated miRNAs from pancreatic {beta} cells reveals a role for miR-30d in insulin transcription, RNA, 15, 287, 10.1261/rna.1211209 · DOI
  30. X Zhao, 2012, MicroRNA-30d induces insulin transcription factor MafA and insulin production by targeting mitogen-activated protein 4 kinase 4 (MAP4K4) in pancreatic beta-cells, The Journal of biological chemistry, 287, 31155, 10.1074/jbc.M112.362632 · DOI
  31. SD Jordan, 2011, Obesity-induced overexpression of miRNA-143 inhibits insulin-stimulated AKT activation and impairs glucose metabolism, Nat Cell Biol, 13, 434, 10.1038/ncb2211 · DOI
  32. W Gao, 2012, Plasma levels of lipometabolism-related miR-122 and miR-370 are increased in patients with hyperlipidemia and associated with coronary artery disease, Lipids Health Dis, 11, 55, 10.1186/1476-511X-11-55 · DOI
  33. P Willeit, 2017, Circulating MicroRNA-122 Is Associated With the Risk of New-Onset Metabolic Syndrome and Type 2 Diabetes, Diabetes, 66, 347, 10.2337/db16-0731 · DOI
  34. NC Salvoza, 2016, Association of Circulating Serum miR-34a and miR-122 with Dyslipidemia among Patients with Non-Alcoholic Fatty Liver Disease, PloS one, 11, e0153497, 10.1371/journal.pone.0153497 · DOI
  35. Lopez YO Nunez, 2017, Gastric bypass surgery with exercise alters plasma microRNAs that predict improvements in cardiometabolic risk, Int J Obes (Lond), 41, 1121, 10.1038/ijo.2017.84 · DOI
  36. V Desgagne, 2017, microRNAs in lipoprotein and lipid metabolism: from biological function to clinical application, Clin Chem Lab Med, 55, 667, 10.1515/cclm-2016-0575 · DOI

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Metadata

Authors
de Candia, P., Spinetti, G., Specchia, C., Sangalli, E., La Sala, L., Uccellatore, A., Lupini, S., Genovese, S., Matarese, G., & Ceriello, A.
ORCID
de Candia, Paola
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2017-12-04
Volume
12
Issue
12
Pages
e0188980
ISSN
1932-6203
DOI
10.1371/journal.pone.0188980
PMC
PMC5714331
Cited by
105
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Fondazione Invernizzi
  • Italian Ministry of Health (RRC-2017-2364948)
  • Juvenile Diabetes Research Foundation International (1-SRA-2018-477-S-B, Lilly program 2015)
  • European Research Council ("menTORinTregs" (n. 310496))

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