Predicting and understanding the response to short-term intensive insulin therapy in people with early type 2 diabetes

Yury O Nunez Lopez, Ravi Retnakaran, Bernard Zinman, Richard E Pratley, Attila A Seyhan, Yury O Nunez Lopez, Ravi Retnakaran, Bernard Zinman, Richard E Pratley, Attila A Seyhan

Abstract

Objective: Short-term intensive insulin therapy (IIT) early in the course of type 2 diabetes acutely improves beta-cell function with long-lasting effects on glycemic control. However, conventional measures cannot determine which patients are better suited for IIT, and little is known about the molecular mechanisms determining response. Therefore, this study aimed to develop a model that could accurately predict the response to IIT and provide insight into molecular mechanisms driving such response in humans.

Methods: Twenty-four patients with early type 2 diabetes were assessed at baseline and four weeks after IIT, consisting of basal detemir and premeal insulin aspart. Twelve individuals had a beneficial beta-cell response to IIT (responders) and 12 did not (nonresponders). Beta-cell function was assessed by multiple methods, including Insulin Secretion-Sensitivity Index-2. MicroRNAs (miRNAs) were profiled in plasma samples before and after IIT. The response to IIT was modeled using a machine learning algorithm and potential miRNA-mediated regulatory mechanisms assessed by differential expression, correlation, and functional network analyses (FNA).

Results: Baseline levels of circulating miR-145-5p, miR-29c-3p, and HbA1c accurately (91.7%) predicted the response to IIT (OR = 121 [95% CI: 6.7, 2188.3]). Mechanistically, a previously described regulatory loop between miR-145-5p and miR-483-3p/5p, which controls TP53-mediated apoptosis, appears to also occur in our study population of humans with early type 2 diabetes. In addition, significant (fold change > 2, P < 0.05) longitudinal changes due to IIT in the circulating levels of miR-138-5p, miR-192-5p, miR-195-5p, miR-320b, and let-7a-5p further characterized the responder group and significantly correlated (|r| > 0.4, P < 0.05) with the changes in measures of beta-cell function and insulin sensitivity. FNA identified a network of coordinately/cooperatively regulated miRNA-targeted genes that potentially drives the IIT response through negative regulation of apoptotic processes that underlie beta cell dysfunction and concomitant positive regulation of proliferation.

Conclusions: Responses to IIT in people with early type 2 diabetes are associated with characteristic miRNA signatures. This study represents a first step to identify potential responders to IIT (a current limitation in the field) and provides important insight into the pathophysiologic determinants of the reversibility of beta-cell dysfunction. ClinicalTrial.gov identifier: NCT01270789.

Keywords: Beta-cell dysfunction; Cooperative overtargeting; MicroRNA; Response prediction; Short-term intensive insulin therapy; Type 2 diabetes.

Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
Identification of an optimal miRNA classifier for response to short-term IIT and summary of performance measures following an iterative random forests (RF) approach. (A–C) Dual plots for features comprising the best classifier of response. (D–G) Correlations between baseline levels of miR-145-5p and changes in measures of beta cell function and glucose control. ISSI-2 was the main outcome measure of beta-cell function, in the parent LIBRA trial, in response to short-term IIT. (H) Sensitivity analysis using the receiver operator characteristic curve (ROC). (I) Odds ratio plot for the classification using the best RF classifier. (J) Confusion matrix for the best RF classifier. (K) Performance measures for the best RF classifier. Orange-filled circles represent individual values for Responder (R) group. Blue-filled circles represent individual values for Nonresponder (NR) group. AUC: area under the ROC curve; PPV: positive predictive value; NPV: negative predictive value; FPR: false positive rate; FNR: false negative rate.
Figure 2
Figure 2
Expression profiling of miR-483-5p and miR-483-3p in circulation of humans with type 2 diabetes. Quantitation using real time PCR and TaqMan® assays. Measurements were normalized against the expression of the internal/endogenous control miR-191. Blue and orange circles represent nonresponder (NR) and responder (R) group data points, respectively, in panels H–L. Black, red, green, and blue represent nonresponder-post, nonresponder-pre, responder-post, and responder-pre, respectively, in panels E–G.
Figure 3
Figure 3
Longitudinal changes and correlations in microRNAs (miRNAs) that significantly change in response to short-term intensive insulin therapy (IIT). (A–E) Pattern A miRNAs with significant Group-by-Time interaction (P < 0.05, FDR < 0.2). Data presented as quantile-normalized log2 fold changes relative to the median of the nonresponders group at pre-IIT (NR.Pre) time point. The connected dots represent corresponding pre-therapy and post-therapy measurements for each individual participant (R: Responder group, orange filling; NR: Nonresponder group, blue filling; n = 12 per group). In the boxplots, the box delineates the first and third quartiles (the interquartile range, IQR), whereas the whiskers delineate the smallest and largest values inside a 1.5 box-length from the end of the box. The boxplot summarizes the data presented in the corresponding side-by-side dot plot, while the dot plot reveals valuable information about the longitudinal changes taking place per subject. Note that some samples have similar normalized logFC values, therefore multiple lines may converge onto a single dot. The number of lines, not the number of dots, represents the number of samples contributing data for the specific plot. Red dots represent data points located at greater than 1.5 IQR from the end of the box. Blue filling used for NR group and orange filling for R group. (F) Principal component analysis plot based on changes in Pattern A miRNAs effectively separate the responders from the nonresponders subjects. (G–I) Correlated changes in the circulating levels of Pattern A miRNAs in response to short-term IIT.
Figure 4
Figure 4
Correlations between changes in Pattern A circulating miRNA levels and changes in relevant clinical variables and indices of interest. Partial correlations calculated using the ppcor R package and adjusting for age, duration of diabetes, baseline HbA1c, and baseline AUC glucagon. Blue-filled circles represent individual values for the Nonresponder (NR) group. Orange-filled circles represent individual values for the Responder (R) group.
Figure 5
Figure 5
Functional network analysis. (A) Network of experimentally validated interactions between seven IIT miRNAs and corresponding 91 overtargeted genes. Overtargeted genes appear to interact with a significantly higher number of miRNAs than expected by chance (as determined by hypergeometric tests of network proportions compared to the respective proportions in the miRNA–target interaction universe/background). (B) Enrichment of gene ontology annotations among the complete set of 91 overtargeted genes (method set to ‘slim’ annotations and significance value cutoff Padj < 0.05). (C) Re-drawn subnetwork for the central cluster of 9 highly overtargeted genes (3 or more targeting miRNAs per gene) and interacting set of six IIT miRNAs. (D) Enrichment of gene ontology annotations for the central cluster of 9 overtargeted genes (method set to ‘all’ annotations and significance value cutoff Padj < 0.001). Categories with the lowest P values at the bottom of the plot. Annotation enrichment analysis was assessed using the GOCluster_Report function of the systemPipeR package in the R environment.

References

    1. Kahn S.E., Zraika S., Utzschneider K.M., Hull R.L. The beta cell lesion in type 2 diabetes: there has to be a primary functional abnormality. Diabetologia. 2009;52:1003–1012.
    1. Wajchenberg B.L. beta-cell failure in diabetes and preservation by clinical treatment. Endocrine Reviews. 2007;28:187–218.
    1. Retnakaran R., Zinman B. Short-term intensified insulin treatment in type 2 diabetes: long-term effects on beta-cell function. Diabetes, Obesity & Metabolism. 2012;14(Suppl 3):161–166.
    1. Kramer C.K., Choi H., Zinman B., Retnakaran R. Glycemic variability in patients with early type 2 diabetes: the impact of improvement in beta-cell function. Diabetes Care. 2014;37:1116–1123.
    1. Weng J., Retnakaran R., Ariachery C.A., Ji L., Meneghini L., Yang W. Short-term intensive insulin therapy at diagnosis in type 2 diabetes: plan for filling the gaps. Diabetes/Metabolism Research and Reviews. 2015;31:537–544.
    1. Ryan E.A., Imes S., Wallace C. Short-term intensive insulin therapy in newly diagnosed type 2 diabetes. Diabetes Care. 2004;27:1028–1032.
    1. Li Y., Xu W., Liao Z., Yao B., Chen X., Huang Z. Induction of long-term glycemic control in newly diagnosed type 2 diabetic patients is associated with improvement of beta-cell function. Diabetes Care. 2004;27:2597–2602.
    1. Weng J., Li Y., Xu W., Shi L., Zhang Q., Zhu D. Effect of intensive insulin therapy on beta-cell function and glycaemic control in patients with newly diagnosed type 2 diabetes: a multicentre randomised parallel-group trial. Lancet (London, England) 2008;371:1753–1760.
    1. Wallia A., Molitch M.E. Insulin therapy for type 2 diabetes mellitus. Journal of the American Medical Association. 2014;311:2315–2325.
    1. Kramer C.K., Zinman B., Retnakaran R. Short-term intensive insulin therapy in type 2 diabetes mellitus: a systematic review and meta-analysis. The Lancet Diabetes & Endocrinology. 2013;1:28–34.
    1. Kramer C.K., Choi H., Zinman B., Retnakaran R. Determinants of reversibility of beta-cell dysfunction in response to short-term intensive insulin therapy in patients with early type 2 diabetes. American Journal of Physiology Endocrinology and Metabolism. 2013;305:E1398–E1407.
    1. Retnakaran R., Yakubovich N., Qi Y., Opsteen C., Zinman B. The response to short-term intensive insulin therapy in type 2 diabetes. Diabetes, Obesity & Metabolism. 2010;12:65–71.
    1. Sayed D., Abdellatif M. MicroRNAs in development and disease. Physiological Reviews. 2011;91:827–887.
    1. Victoria B., Nunez Lopez Y.O., Masternak M.M. MicroRNAs and the metabolic hallmarks of aging. Molecular and Cellular Endocrinology. 2017;455:131–147.
    1. Shantikumar S., Caporali A., Emanueli C. Role of microRNAs in diabetes and its cardiovascular complications. Cardiovascular Research. 2012;93:583–593.
    1. Guay C., Regazzi R. Role of islet microRNAs in diabetes: which model for which question? Diabetologia. 2015;58:456–463.
    1. Prats-Puig A., Ortega F.J., Mercader J.M., Moreno-Navarrete J.M., Moreno M., Bonet N. Changes in circulating microRNAs are associated with childhood obesity. The Journal of Clinical Endocrinology and Metabolism. 2013;98:E1655–E1660.
    1. Nunez Lopez Y.O., Garufi G., Seyhan A.A. Altered levels of circulating cytokines and microRNAs in lean and obese individuals with prediabetes and type 2 diabetes. Molecular BioSystems. 2016;13:106–121.
    1. Jones A., Danielson K.M., Benton M.C., Ziegler O., Shah R., Stubbs R.S. miRNA signatures of insulin resistance in obesity. Obesity (Silver Spring, Md) 2017;25:1734–1744.
    1. Nunez Lopez Y.O., Garufi G., Pasarica M., Seyhan A.A. Elevated and correlated expressions of miR-24, miR-30d, miR-146a, and SFRP-4 in human abdominal adipose tissue play a role in adiposity and insulin resistance. International Journal of Endocrinology. 2018;2018:7.
    1. Ortega F.J., Mercader J.M., Catalan V., Moreno-Navarrete J.M., Pueyo N., Sabater M. Targeting the circulating microRNA signature of obesity. Clinical Chemistry. 2013;59:781–792.
    1. Zampetaki A., Kiechl S., Drozdov I., Willeit P., Mayr U., Prokopi M. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circulation Research. 2010;107:810–817.
    1. Ortega F.J., Mercader J.M., Moreno-Navarrete J.M., Rovira O., Guerra E., Esteve E. Profiling of circulating microRNAs reveals common microRNAs linked to type 2 diabetes that change with insulin sensitization. Diabetes Care. 2014;37:1375–1383.
    1. Seyhan A.A., Nunez Lopez Y.O., Xie H., Yi F., Mathews C., Pasarica M. Pancreas-enriched miRNAs are altered in the circulation of subjects with diabetes: a pilot cross-sectional study. Scientific Reports. 2016;6:31479.
    1. He Y., Ding Y., Liang B., Lin J., Kim T.K., Yu H. A systematic study of dysregulated microRNA in type 2 diabetes mellitus. International Journal of Molecular Sciences. 2017;18
    1. Copier C.U., León L., Fernández M., Contador D., Calligaris S.D. Circulating miR-19b and miR-181b are potential biomarkers for diabetic cardiomyopathy. Scientific Reports. 2017;7
    1. Catanzaro G., Besharat Z.M., Chiacchiarini M., Abballe L., Sabato C., Vacca A. Circulating microRNAs in elderly type 2 diabetic patients. International Journal of Endocrinology. 2018;2018:11.
    1. Retnakaran R., Kramer C.K., Choi H., Swaminathan B., Zinman B. Liraglutide and the preservation of pancreatic beta-cell function in early type 2 diabetes: the LIBRA trial. Diabetes Care. 2014;37:3270–3278.
    1. Kramer C.K., Zinman B., Choi H., Connelly P.W., Retnakaran R. Impact of the glucagon assay when assessing the effect of chronic liraglutide therapy on glucagon secretion. The Journal of Clinical Endocrinology and Metabolism. 2017
    1. Breiman L. Random forests. Machine Learning. 2001;45:5–32.
    1. R Core Team . R Foundation for Statistical Computing; Vienna, Austria: 2018. R: a language and environment for statistical computing.
    1. Kirschner M.B., Edelman J.B., Kao S.C.H., Vallely M.P., van Zandwijk N., Reid G. The impact of hemolysis on cell-free microRNA biomarkers. Frontiers in Genetics. 2013;4:94.
    1. Shah J.S., Soon P.S., Marsh D.J. Comparison of methodologies to detect low levels of hemolysis in serum for accurate assessment of serum microRNAs. PLoS One. 2016;11:e0153200.
    1. Blondal T., Jensby Nielsen S., Baker A., Andreasen D., Mouritzen P., Wrang Teilum M. Assessing sample and miRNA profile quality in serum and plasma or other biofluids. Methods (San Diego, Calif) 2013;59:S1–S6.
    1. Strobl C., Malley J., Tutz G. An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychological Methods. 2009;14:323–348.
    1. Marinic I., Supek F., Kovacic Z., Rukavina L., Jendricko T., Kozaric-Kovacic D. Posttraumatic stress disorder: diagnostic data analysis by data mining methodology. Croatian Medical Journal. 2007;48:185–197.
    1. Liaw A., Wiener M. Classification and regression by randomForest. R News. 2002;2
    1. Díaz-Uriarte R., Alvarez de Andrés S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics. 2006;7:3.
    1. Laurent L.C., Chen J., Ulitsky I., Mueller F.J., Lu C., Shamir R. Comprehensive microRNA profiling reveals a unique human embryonic stem cell signature dominated by a single seed sequence. Stem cells (Dayton, Ohio) 2008;26:1506–1516.
    1. Lewohl J.M., Nunez Y.O., Dodd P.R., Tiwari G.R., Harris R.A., Mayfield R.D. Up-regulation of microRNAs in brain of human alcoholics. Alcoholism: Clinical and Experimental Research. 2011;35:1928–1937.
    1. Xiao Y., Xu C., Guan J., Ping Y., Fan H., Li Y. Discovering dysfunction of multiple microRNAs cooperation in disease by a conserved microRNA co-expression network. PLoS One. 2012;7:e32201.
    1. Nunez Y.O., Truitt J.M., Gorini G., Ponomareva O.N., Blednov Y.A., Harris R.A. Positively correlated miRNA-mRNA regulatory networks in mouse frontal cortex during early stages of alcohol dependence. BMC Genomics. 2013;14:725.
    1. Gorini G., Nunez Y.O., Mayfield R.D. Integration of miRNA and protein profiling reveals coordinated neuroadaptations in the alcohol-dependent mouse brain. PLoS One. 2013;8:e82565.
    1. Victoria Martinez B., Dhahbi J.M., Nunez Lopez Y.O., Lamperska K., Golusinski P., Luczewski L. Circulating small non-coding RNA signature in head and neck squamous cell carcinoma. Oncotarget. 2015;6:19246–19263.
    1. Victoria B., Dhahbi J.M., Nunez Lopez Y.O., Spinel L., Atamna H., Spindler S.R. Circulating microRNA signature of genotype-by-age interactions in the long-lived Ames dwarf mouse. Aging Cell. 2015;14:1055–1066.
    1. Shao T., Wang G., Chen H., Xie Y., Jin X., Bai J. Survey of miRNA-miRNA cooperative regulation principles across cancer types. Briefings in Bioinformatics. 2018
    1. Cava C., Colaprico A., Bertoli G., Graudenzi A., Silva T.C., Olsen C. SpidermiR: an R/bioconductor package for integrative analysis with miRNA data. International Journal of Molecular Sciences. 2017;18:274.
    1. Demchak B., Hull T., Reich M., Liefeld T., Smoot M., Ideker T. Cytoscape: the network visualization tool for GenomeSpace workflows. F1000Research. 2014;3:151.
    1. Backman T.W.H., Girke T. systemPipeR: NGS workflow and report generation environment. BMC Bioinformatics. 2016;17:388.
    1. Nunez Lopez Y.O., Pittas A.G., Pratley R.E., Seyhan A.A. Circulating levels of miR-7, miR-152 and miR-192 respond to vitamin D supplementation in adults with prediabetes and correlate with improvements in glycemic control. The Journal of Nutritional Biochemistry. 2017;49:117–122.
    1. Nunez Lopez Y.O., Coen P.M., Goodpaster B.H., Seyhan A.A. Gastric bypass surgery with exercise alters plasma microRNAs that predict improvements in cardiometabolic risk. International Journal of Obesity. 2017;41:1121–1130.
    1. Lupini L., Pepe F., Ferracin M., Braconi C., Callegari E., Pagotto S. Over-expression of the miR-483-3p overcomes the miR-145/TP53 pro-apoptotic loop in hepatocellular carcinoma. Oncotarget. 2016;7:31361–31371.
    1. Mohan R., Mao Y., Zhang S., Zhang Y.W., Xu C.R., Gradwohl G. Differentially expressed microRNA-483 confers distinct functions in pancreatic beta- and alpha-cells. Journal of Biological Chemistry. 2015;290:19955–19966.
    1. Brunzell J.D., Robertson R.P., Lerner R.L., Hazzard W.R., Ensinck J.W., Bierman E.L. Relationships between fasting plasma glucose levels and insulin secretion during intravenous glucose tolerance tests. The Journal of Clinical Endocrinology and Metabolism. 1976;42:222–229.
    1. Jordan S.D., Kruger M., Willmes D.M., Redemann N., Wunderlich F.T., Bronneke H.S. Obesity-induced overexpression of miRNA-143 inhibits insulin-stimulated AKT activation and impairs glucose metabolism. Nature Cell Biology. 2011;13:434–446.
    1. Kang M.H., Zhang L.H., Wijesekara N., de Haan W., Butland S., Bhattacharjee A. Regulation of ABCA1 protein expression and function in hepatic and pancreatic islet cells by miR-145. Arteriosclerosis, Thrombosis, and Vascular Biology. 2013;33:2724–2732.
    1. Wen F., Yang Y., Jin D., Sun J., Yu X., Yang Z. MiRNA-145 is involved in the development of resistin-induced insulin resistance in HepG2 cells. Biochemical and Biophysical Research Communications. 2014;445:517–523.
    1. Lorente-Cebrian S., Mejhert N., Kulyte A., Laurencikiene J., Astrom G., Heden P. MicroRNAs regulate human adipocyte lipolysis: effects of miR-145 are linked to TNF-alpha. PLoS One. 2014;9:e86800.
    1. Cui C., Ye X., Chopp M., Venkat P., Zacharek A., Yan T. miR-145 regulates diabetes-bone marrow stromal cell-induced neurorestorative effects in diabetes stroke rats. Stem Cells Translational Medicine. 2016;5:1656–1667.
    1. Dooley J., Garcia-Perez J.E., Sreenivasan J., Schlenner S.M., Vangoitsenhoven R., Papadopoulou A.S. The microRNA-29 family dictates the balance between homeostatic and pathological glucose handling in diabetes and obesity. Diabetes. 2016;65:53–61.
    1. Arnold N., Koppula P.R., Gul R., Luck C., Pulakat L. Regulation of cardiac expression of the diabetic marker microRNA miR-29. PLoS One. 2014;9:e103284.
    1. Slusarz A., Pulakat L. The two faces of miR-29. Journal of Cardiovascular Medicine (Hagerstown, Md) 2015;16:480–490.
    1. Kurtz C.L., Peck B.C., Fannin E.E., Beysen C., Miao J., Landstreet S.R. MicroRNA-29 fine-tunes the expression of key FOXA2-activated lipid metabolism genes and is dysregulated in animal models of insulin resistance and diabetes. Diabetes. 2014;63:3141–3148.
    1. Zhang Y., Fan M., Zhang X., Huang F., Wu K., Zhang J. Cellular microRNAs up-regulate transcription via interaction with promoter TATA-box motifs. RNA (New York, NY) 2014;20:1878–1889.
    1. van de Bunt M., Gaulton K.J., Parts L., Moran I., Johnson P.R., Lindgren C.M. The miRNA profile of human pancreatic islets and beta-cells and relationship to type 2 diabetes pathogenesis. PLoS One. 2013;8:e55272.
    1. Sun Y., Koo S., White N., Peralta E., Esau C., Dean N.M. Development of a micro-array to detect human and mouse microRNAs and characterization of expression in human organs. Nucleic Acids Research. 2004;32:e188.
    1. Parrizas M., Brugnara L., Esteban Y., Gonzalez-Franquesa A., Canivell S., Murillo S. Circulating miR-192 and miR-193b are markers of prediabetes and are modulated by an exercise intervention. The Journal of Clinical Endocrinology and Metabolism. 2014 jc20142574.
    1. Shah R., Murthy V., Pacold M., Danielson K., Tanriverdi K., Larson M.G. Extracellular RNAs are associated with insulin resistance and metabolic phenotypes. Diabetes Care. 2017;40:546–553.
    1. Kato M., Zhang J., Wang M., Lanting L., Yuan H., Rossi J.J. MicroRNA-192 in diabetic kidney glomeruli and its function in TGF-beta-induced collagen expression via inhibition of E-box repressors. Proceedings of the National Academy of Sciences of the United States of America. 2007;104:3432–3437.
    1. Pirola C.J., Fernandez Gianotti T., Castano G.O., Mallardi P., San Martino J., Mora Gonzalez Lopez Ledesma M. Circulating microRNA signature in non-alcoholic fatty liver disease: from serum non-coding RNAs to liver histology and disease pathogenesis. Gut. 2015;64:800–812.
    1. Ling H.Y., Ou H.S., Feng S.D., Zhang X.Y., Tuo Q.H., Chen L.X. Changes in microRNA (miR) profile and effects of miR-320 in insulin-resistant 3T3-L1 adipocytes. Clinical and Experimental Pharmacology & Physiology. 2009;36:e32–e39.
    1. Herrera B.M., Lockstone H.E., Taylor J.M., Ria M., Barrett A., Collins S. Global microRNA expression profiles in insulin target tissues in a spontaneous rat model of type 2 diabetes. Diabetologia. 2010;53:1099–1109.
    1. Karolina D.S., Tavintharan S., Armugam A., Sepramaniam S., Pek S.L., Wong M.T. Circulating miRNA profiles in patients with metabolic syndrome. The Journal of Clinical Endocrinology and Metabolism. 2012;97:E2271–E2276.
    1. Wang X., Huang W., Liu G., Cai W., Millard R.W., Wang Y. Cardiomyocytes mediate anti-angiogenesis in type 2 diabetic rats through the exosomal transfer of miR-320 into endothelial cells. Journal of Molecular and Cellular Cardiology. 2014;74:139–150.
    1. Santovito D., De Nardis V., Marcantonio P., Mandolini C., Paganelli C., Vitale E. Plasma exosome microRNA profiling unravels a new potential modulator of adiponectin pathway in diabetes: effect of glycemic control. The Journal of Clinical Endocrinology and Metabolism. 2014;99:E1681–E1685.
    1. Guay C., Menoud V., Rome S., Regazzi R. Horizontal transfer of exosomal microRNAs transduce apoptotic signals between pancreatic beta-cells. Cell Communication and Signaling. 2015;13:17.
    1. Costantino S., Paneni F., Luscher T.F., Cosentino F. MicroRNA profiling unveils hyperglycaemic memory in the diabetic heart. European Heart Journal. 2016;37:572–576.
    1. Marques F.Z., Vizi D., Khammy O., Mariani J.A., Kaye D.M. The transcardiac gradient of cardio-microRNAs in the failing heart. European Journal of Heart Failure. 2016;18:1000–1008.
    1. Zhou X., Sun F., Luo S., Zhao W., Yang T., Zhang G. Let-7a is an antihypertrophic regulator in the heart via targeting calmodulin. International Journal of Biological Sciences. 2017;13:22–31.
    1. Hannafon B.N., Carpenter K.J., Berry W.L., Janknecht R., Dooley W.C., Ding W.Q. Exosome-mediated microRNA signaling from breast cancer cells is altered by the anti-angiogenesis agent docosahexaenoic acid (DHA) Molecular Cancer. 2015;14:133.
    1. Li Y., Tang X., He Q., Yang X., Ren X., Wen X. Overexpression of mitochondria mediator gene TRIAP1 by miR-320b loss is associated with progression in nasopharyngeal carcinoma. PLoS Genetics. 2016;12:e1006183.
    1. Serguienko A., Grad I., Wennerstrom A.B., Meza-Zepeda L.A., Thiede B., Stratford E.W. Metabolic reprogramming of metastatic breast cancer and melanoma by let-7a microRNA. Oncotarget. 2015;6:2451–2465.
    1. Nunez Lopez Y.O., Victoria B., Golusinski P., Golusinski W., Masternak M.M. Characteristic miRNA expression signature and random forest survival analysis identify potential cancer-driving miRNAs in a broad range of head and neck squamous cell carcinoma subtypes. Reports of practical oncology and radiotherapy. Journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology. 2018;23:6–20.
    1. Frost R.J.A., Olson E.N. Control of glucose homeostasis and insulin sensitivity by the <em>Let-7</em> family of microRNAs. Proceedings of the National Academy of Sciences of the United States of America. 2011;108:21075–21080.
    1. Nunez Lopez Y.O., Pratley R.E. American Diabetes Association; Orlando, FL: 2018. Pioglitazone alters the cargo composition of circulating exosomes in subjects with type 2 diabetes. (American Diabetes Association's 78th Scientific Sessions).
    1. Dahlmans D., Houzelle A., Andreux P., Jorgensen J.A., Wang X., de Windt L.J. An unbiased silencing screen in muscle cells identifies miR-320a, miR-150, miR-196b, and miR-34c as regulators of skeletal muscle mitochondrial metabolism. Molecular Metabolism. 2017;6:1429–1442.
    1. Holman R.R., Paul S.K., Bethel M.A., Matthews D.R., Neil H.A. 10-year follow-up of intensive glucose control in type 2 diabetes. New England Journal of Medicine. 2008;359:1577–1589.
    1. Kawata K., Hatano A., Yugi K., Kubota H., Sano T., Fujii M. Trans-omic analysis reveals selective responses to induced and basal insulin across signaling, transcriptional, and metabolic networks. iScience. 2018;7:212–229.
    1. Daitoku H., Sakamaki J., Fukamizu A. Regulation of FoxO transcription factors by acetylation and protein-protein interactions. Biochimica et Biophysica Acta. 2011;1813:1954–1960.
    1. Wang R.H., Xu X., Kim H.S., Xiao Z., Deng C.X. SIRT1 deacetylates FOXA2 and is critical for Pdx1 transcription and beta-cell formation. International Journal of Biological Sciences. 2013;9:934–946.
    1. Yalley A., Schill D., Hatta M., Johnson N., Cirillo L.A. Loss of interdependent binding by the FoxO1 and FoxA1/A2 forkhead transcription factors culminates in perturbation of active chromatin marks and binding of transcriptional regulators at insulin-sensitive genes. Journal of Biological Chemistry. 2016;291:8848–8861.
    1. Thomou T., Mori M.A., Dreyfuss J.M., Konishi M., Sakaguchi M., Wolfrum C. Adipose-derived circulating miRNAs regulate gene expression in other tissues. Nature. 2017
    1. Cansell C., Luquet S. Hypothalamic regulation of energy balance: a key role for DICER miRNA processing in arcuate POMC neurons. Molecular Metabolism. 2012;2:55–57.
    1. Schneeberger M., Altirriba J., Garcia A., Esteban Y., Castano C., Garcia-Lavandeira M. Deletion of miRNA processing enzyme Dicer in POMC-expressing cells leads to pituitary dysfunction, neurodegeneration and development of obesity. Molecular Metabolism. 2012;2:74–85.
    1. Lehrke M., Lazar M.A. The many faces of PPARγ. Cell. 2005;123:993–999.
    1. Rosen E.D., Sarraf P., Troy A.E., Bradwin G., Moore K., Milstone D.S. PPARγ is required for the differentiation of adipose tissue in vivo and in vitro. Molecular Cell. 1999;4:611–617.
    1. Sharma A.M., Staels B. Review: Peroxisome proliferator-activated receptor gamma and adipose tissue – understanding obesity-related changes in regulation of lipid and glucose metabolism. The Journal of Clinical Endocrinology and Metabolism. 2007;92:386–395.
    1. Taylor R., Al-Mrabeh A., Zhyzhneuskaya S., Peters C., Barnes A.C., Aribisala B.S. Remission of human type 2 diabetes requires decrease in liver and pancreas fat content but is dependent upon capacity for beta cell recovery. Cell Metabolism. 2018;28:547–556 e543.
    1. Guay C., Regazzi R. Circulating microRNAs as novel biomarkers for diabetes mellitus. Nature Reviews Endocrinology. 2013;9:513–521.
    1. Yang W.M., Jeong H.J., Park S.W., Lee W. Obesity-induced miR-15b is linked causally to the development of insulin resistance through the repression of the insulin receptor in hepatocytes. Molecular Nutrition & Food Research. 2015;59:2303–2314.
    1. Kornfeld J.W., Baitzel C., Konner A.C., Nicholls H.T., Vogt M.C., Herrmanns K. Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature. 2013;494:111–115.

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