Circulating MicroRNAs predict glycemic improvement and response to a behavioral intervention

Elena Flowers, Isabel Elaine Allen, Alka M Kanaya, Bradley E Aouizerat, Elena Flowers, Isabel Elaine Allen, Alka M Kanaya, Bradley E Aouizerat

Abstract

Background: MicroRNAs may be important regulators of risk for type 2 diabetes. The purpose of this longitudinal observational study was to assess whether circulating microRNAs predicted improvements in fasting blood glucose, a major risk factor for type 2 diabetes, over 12 months.

Methods: The study included participants (n = 82) from a previously completed trial that tested the effect of restorative yoga on individuals with prediabetes. Circulating microRNAs were measured using a flow cytometry miRNA assay. Linear models were used to determine the optimal sets of microRNA predictors overall and by intervention group.

Results: Subsets of microRNAs were significant predictors of final fasting blood glucose after 12-months (R2 = 0.754, p < 0.001) and changes in fasting blood glucose over 12-months (R2 = 0.731, p < 0.001). Three microRNAs (let-7c, miR-363, miR-374b) were significant for the control group only, however there was no significant interaction by intervention group.

Conclusions: Circulating microRNAs are significant predictors of fasting blood glucose in individuals with prediabetes. Among the identified microRNAs, several have previously been associated with risk for type 2 diabetes. This is one of the first studies to use a longitudinal design to assess whether microRNAs predict changes in fasting blood glucose over time. Further exploration of the function of the microRNAs included in these models may provide new insights about the complex etiology of type 2 diabetes and responses to behavioral risk reduction interventions.

Trial registration: This study was a secondary analysis of a previously completed clinical trial that is registered at clinicaltrials.gov (NCT01024816) on December 3, 2009.

Keywords: Biomarker; Diabetes; Fasting blood glucose; Yoga; microRNA.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Box and Whisker Plots Depicting Variability in MicroRNA Expression Levels. Within each box, the bottom border represents the 25th percentile, the center line represents the 50th percentile, and the upper border represents the 75th percentile. The lowest horizontal line represents the minimum, and the upper horizontal line represents the maximum. Small black circles represent outliers. Individual microRNAs are represented on the x-axis. Arbitrary florescence units (AU) are represented on the y-axis. Because the overall range of AU was large, subsets of microRNAs were grouped into panels. Panel A shows microRNAs with median AUs  1000

References

    1. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843. doi: 10.1016/j.diabres.2019.107843.
    1. National Diabetes Statistics Report. Atlanta, GA: Centers for Diseaes Control and Prevention, U.S. Department of Health and Human Services; 2017.
    1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53. doi: 10.1038/nature08494.
    1. Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9(1):2941. doi: 10.1038/s41467-018-04951-w.
    1. 2. Classification and Diagnosis of Diabetes: <em> Standards of Medical Care in Diabetes—2020</em>. Diabetes Care. 2020;43(Supplement 1):S14-S31.
    1. Flowers E, Won GY, Fukuoka Y. MicroRNAs associated with exercise and diet: a systematic review. Physiol Genom. 2015;47(1):1–11. doi: 10.1152/physiolgenomics.00095.2014.
    1. Parr EB, Camera DM, Burke LM, Phillips SM, Coffey VG, Hawley JA, et al. Circulating MicroRNA Responses between ‘High’ and ‘Low’ Responders to a 16-Wk Diet and Exercise Weight Loss Intervention. PLOS ONE. 2016;11:e0152545. doi: 10.1371/journal.pone.0152545.
    1. Vaishya S, Sarwade RD, Seshadri V. MicroRNA, Proteins, and Metabolites as Novel Biomarkers for Prediabetes, Diabetes, and Related Complications. Frontiers in Endocrinology. 2018;9:180. doi: 10.3389/fendo.2018.00180.
    1. Jahantigh D, Mirani Sargazi F, Sargazi S, Saravani R, Ghazaey Zidanloo S, Heidari Nia M, et al. Relationship between Functional miR-143/145 Cluster Variants and Susceptibility to Type 2 Diabetes Mellitus: A Preliminary Case-Control Study and Bioinformatics Analyses. Endocr Res. 2021:1–11.
    1. Higuchi C, Nakatsuka A, Eguchi J, Teshigawara S, Kanzaki M, Katayama A, et al. Identification of circulating miR-101, miR-375 and miR-802 as biomarkers for type 2 diabetes. Metabolism. 2015;64(4):489–97. doi: 10.1016/j.metabol.2014.12.003.
    1. Flowers E, Aouizerat BE, Abbasi F, Lamendola C, Grove KM, Fukuoka Y, et al. Circulating microRNA-320a and microRNA-486 predict thiazolidinedione response: Moving towards precision health for diabetes prevention. Metabolism. 2015.
    1. Flowers E, Gadgil M, Aouizerat BE, Kanaya AM. Circulating micrornas associated with glycemic impairment and progression in Asian Indians. Biomark Res. 2015;3(1):1–8. doi: 10.1186/s40364-015-0047-y.
    1. Kanaya AM, Araneta MR, Pawlowsky SB, Barrett-Connor E, Grady D, Vittinghoff E, et al. Restorative yoga and metabolic risk factors: the Practicing Restorative Yoga vs. Stretching for the Metabolic Syndrome (PRYSMS) randomized trial. J Diabetes Complications. 2014;28(3):406–12. doi: 10.1016/j.jdiacomp.2013.12.001.
    1. Corey SM, Epel E, Schembri M, Pawlowsky SB, Cole RJ, Araneta MR, et al. Effect of restorative yoga vs. stretching on diurnal cortisol dynamics and psychosocial outcomes in individuals with the metabolic syndrome: The PRYSMS randomized controlled trial. Psychoneuroendocrinology. 2014;49 C:260–71.
    1. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and international association for the Study of Obesity. Circulation. 2009;120(16):1640–5. doi: 10.1161/CIRCULATIONAHA.109.192644.
    1. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. doi: 10.1093/clinchem/18.6.499.
    1. Allen M. The SAGE encyclopedia of communication research methods. Los Angeles: SAGE; 2017.
    1. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):RESEARCH0034. doi: 10.1186/gb-2002-3-7-research0034.
    1. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4.
    1. Guo L, Yang S, Zhao Y, Zhang H, Wu Q, Chen F. Global analysis of miRNA gene clusters and gene families reveals dynamic and coordinated expression. Biomed Res Int. 2014;2014:782490.
    1. Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018;46(D1):D296–302. doi: 10.1093/nar/gkx1067.
    1. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. doi: 10.1093/nar/28.1.27.
    1. Majithia AR, Flannick J, Shahinian P, Guo M, Bray MA, Fontanillas P, et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci U S A. 2014;111(36):13127–32. doi: 10.1073/pnas.1410428111.
    1. Flowers E, Kanaya AM, Fukuoka Y, Allen IE, Cooper B, Aouizerat BE. Preliminary evidence supports circulating microRNAs as prognostic biomarkers for type 2 diabetes. Obes Sci Pract. 2017;3(4):446–52. doi: 10.1002/osp4.134.
    1. Chen YH, Heneidi S, Lee JM, Layman LC, Stepp DW, Gamboa GM, et al. miRNA-93 inhibits GLUT4 and is overexpressed in adipose tissue of polycystic ovary syndrome patients and women with insulin resistance. Diabetes. 2013;62(7):2278–86. doi: 10.2337/db12-0963.
    1. Fox CS, Liu Y, White CC, Feitosa M, Smith AV, Heard-Costa N, et al. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet. 2012;8(5):e1002695. doi: 10.1371/journal.pgen.1002695.
    1. Huang X, Liu G, Guo J, Su Z. The PI3K/AKT pathway in obesity and type 2 diabetes. Int J Biol Sci. 2018;14(11):1483–96. doi: 10.7150/ijbs.27173.
    1. Langi G, Szczerbinski L, Kretowski A. Meta-Analysis of Differential miRNA Expression after Bariatric Surgery. J Clin Med. 2019;8:8. doi: 10.3390/jcm8081220.
    1. Esteves JV, Enguita FJ, Machado UF. MicroRNAs-Mediated Regulation of Skeletal Muscle GLUT4 Expression and Translocation in Insulin Resistance. J Diabetes Res. 2017;2017:7267910. doi: 10.1155/2017/7267910.
    1. Shi C, Huang F, Gu X, Zhang M, Wen J, Wang X, et al. Adipogenic miRNA and meta-signature miRNAs involved in human adipocyte differentiation and obesity. Oncotarget. 2016;7(26):40830–45. doi: 10.18632/oncotarget.8518.
    1. Docherty NG, Murphy M, Martin F, Brennan EP, Godson C. Targeting cellular drivers and counter-regulators of hyperglycaemia- and transforming growth factor-beta1-associated profibrotic responses in diabetic kidney disease. Exp Physiol. 2014;99(9):1154–62. doi: 10.1113/expphysiol.2014.078774.
    1. Essandoh K, Li Y, Huo J, Fan GC. MiRNA-Mediated Macrophage Polarization and its Potential Role in the Regulation of Inflammatory Response. Shock. 2016;46(2):122–31. doi: 10.1097/SHK.0000000000000604.
    1. Sun B, Cao Q, Meng M, Wang X. MicroRNA-186-5p serves as a diagnostic biomarker in atherosclerosis and regulates vascular smooth muscle cell proliferation and migration. Cell Mol Biol Lett. 2020;25:27. doi: 10.1186/s11658-020-00220-1.
    1. Xing X, Li Z, Yang X, Li M, Liu C, Pang Y, et al. Adipose-derived mesenchymal stem cells-derived exosome-mediated microRNA-342-5p protects endothelial cells against atherosclerosis. Aging. 2020;12(4):3880–98. doi: 10.18632/aging.102857.
    1. Gao C, Qian H, Shi Q, Zhang H. MicroRNA-363-3p serves as a diagnostic biomarker of acute myocardial infarction and regulates vascular endothelial injury by targeting KLF2. Cardiovasc Diagn Ther. 2020;10(3):421–30. doi: 10.21037/cdt-19-700.
    1. Wang W, Ma F, Zhang H. MicroRNA-374 is a potential diagnostic biomarker for atherosclerosis and regulates the proliferation and migration of vascular smooth muscle cells. Cardiovasc Diagn Ther. 2020;10(4):687–94. doi: 10.21037/cdt-20-444.
    1. Chang YJ, Li YS, Wu CC, Wang KC, Huang TC, Chen Z, et al. Extracellular MicroRNA-92a Mediates Endothelial Cell-Macrophage Communication. Arterioscler Thromb Vasc Biol. 2019;39(12):2492–504. doi: 10.1161/ATVBAHA.119.312707.
    1. Liu X, Wang Y, Zhang X, Zhang X, Guo J, Zhou J, et al. MicroRNA-296-5p promotes healing of diabetic wound by targeting sodium-glucose transporter 2 (SGLT2) Diabetes Metab Res Rev. 2019;35(2):e3104. doi: 10.1002/dmrr.3104.
    1. Xiao D, Hu Y, Fu Y, Wang R, Zhang H, Li M, et al. Emodin improves glucose metabolism by targeting microRNA-20b in insulin-resistant skeletal muscle. Phytomedicine. 2019;59:152758. doi: 10.1016/j.phymed.2018.11.018.
    1. Wang S, Du S, Lv Y, Wang W, Zhang F. Elevated microRNA-20b-3p and reduced thioredoxin-interacting protein ameliorate diabetic retinopathy progression by suppressing the NLRP3 inflammasomes. IUBMB Life. 2020;72(7):1433–48. doi: 10.1002/iub.2267.
    1. Zhen W, Hui D, Wenying S, Yulong S. MicroRNA-20b-5p regulates propofol-preconditioning-induced inhibition of autophagy in hypoxia-and-reoxygenation-stimulated endothelial cells. J Biosci. 2020;45.
    1. Thibonnier M, Esau C, Ghosh S, Wargent E, Stocker C. Metabolic and energetic benefits of microRNA-22 inhibition. BMJ Open Diabetes Res Care. 2020;8(1).
    1. Wang C, Liu G, Yang H, Guo S, Wang H, Dong Z, et al. MALAT1-mediated recruitment of the histone methyltransferase EZH2 to the microRNA-22 promoter leads to cardiomyocyte apoptosis in diabetic cardiomyopathy. Sci Total Environ. 2020:142191.
    1. Mitchell F, Diabetes PTEN mutations increase insulin sensitivity and obesity. Nat Rev Endocrinol. 2012;8(12):698. doi: 10.1038/nrendo.2012.186.
    1. de Castro Barbosa T, Ingerslev LR, Alm PS, Versteyhe S, Massart J, Rasmussen M, et al. High-fat diet reprograms the epigenome of rat spermatozoa and transgenerationally affects metabolism of the offspring. Mol Metab. 2016;5(3):184–97. doi: 10.1016/j.molmet.2015.12.002.

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