Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-569ts Total loading time: 0.248 Render date: 2022-10-02T02:52:48.464Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": true, "useSa": true } hasContentIssue true

Circulating microRNAs from early childhood and adolescence are associated with pre-diabetes at 18 years of age in women from the PMNS cohort

Published online by Cambridge University Press:  22 April 2022

Mugdha V. Joglekar
Affiliation:
Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
Pooja S. Kunte
Affiliation:
Diabetes Unit, KEM Hospital and Research Center, Pune, India
Wilson K.M. Wong
Affiliation:
Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
Dattatray. S. Bhat
Affiliation:
Diabetes Unit, KEM Hospital and Research Center, Pune, India
Sarang N. Satoor
Affiliation:
DNA Sequencing Facility, National Centre for Cell Science, NCMR Campus, Pune, India
Rohan R. Patil
Affiliation:
DY Patil Medical College, DY Patil University, Pune, India
Mahesh S. Karandikar
Affiliation:
DY Patil Medical College, DY Patil University, Pune, India
Caroline H. D. Fall
Affiliation:
MRC Lifecourse Epidemiology Unit, Southampton University and General Hospital, Southampton, UK
Chittaranjan S. Yajnik*
Affiliation:
Diabetes Unit, KEM Hospital and Research Center, Pune, India
Anandwardhan A. Hardikar*
Affiliation:
Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
*
Addresses for correspondence: Anandwardhan A. Hardikar, Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Campbelltown, NSW2560, Australia. Email: A.Hardikar@westernsydney.edu.au; Chittaranjan S. Yajnik, KEM Hospital and Research Center, Pune, India. Email: csyajnik@gmail.com
Addresses for correspondence: Anandwardhan A. Hardikar, Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Campbelltown, NSW2560, Australia. Email: A.Hardikar@westernsydney.edu.au; Chittaranjan S. Yajnik, KEM Hospital and Research Center, Pune, India. Email: csyajnik@gmail.com

Abstract

With type 2 diabetes presenting at younger ages, there is a growing need to identify biomarkers of future glucose intolerance. A high (20%) prevalence of glucose intolerance at 18 years was seen in women from the Pune Maternal Nutrition Study (PMNS) birth cohort. We investigated the potential of circulating microRNAs in risk stratification for future pre-diabetes in these women. Here, we provide preliminary longitudinal analyses of circulating microRNAs in normal glucose tolerant (NGT@18y, N = 10) and glucose intolerant (N = 8) women (ADA criteria) at 6, 12 and 17 years of their age using discovery analysis (OpenArray™ platform). Machine-learning workflows involving Lasso with bootstrapping/leave-one-out cross-validation identified microRNAs associated with glucose intolerance at 18 years of age. Several microRNAs, including miR-212-3p, miR-30e-3p and miR-638, stratified glucose-intolerant women from NGT at childhood. Our results suggest that circulating microRNAs, longitudinally assessed over 17 years of life, are dynamic biomarkers associated with and predictive of pre-diabetes at 18 years of age. Validation of these findings in males and remaining participants from the PMNS birth cohort will provide a unique opportunity to study novel epigenetic mechanisms in the life-course progression of glucose intolerance and enhance current clinical risk prediction of pre-diabetes and progression to type 2 diabetes.

Type
Brief Reports
Copyright
© The Author(s), 2022. Published by Cambridge University Press in association with International Society for Developmental Origins of Health and Disease

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bergman, M, Abdul-Ghani, M, DeFronzo, RA, et al. Review of methods for detecting glycemic disorders. Diabetes Res Clin Pract. 2020; 165, 108233. DOI 10.1016/j.diabres.2020.108233.CrossRefGoogle ScholarPubMed
Bell, K, Shaw, JE, Maple-Brown, L, et al. A position statement on screening and management of prediabetes in adults in primary care in Australia. Diabetes Res Clin Pract. 2020; 164, 108188. DOI 10.1016/j.diabres.2020.108188.CrossRefGoogle ScholarPubMed
Campbell, MD, Sathish, T, Zimmet, PZ, et al. Benefit of lifestyle-based T2DM prevention is influenced by prediabetes phenotype. Nat Rev Endocrinol. 2020; 16(7), 395400. DOI 10.1038/s41574-019-0316-1.CrossRefGoogle ScholarPubMed
Rao, S, Yajnik, CS, Kanade, A, et al. Intake of micronutrient-rich foods in rural Indian mothers is associated with the size of their babies at birth: Pune Maternal Nutrition Study. J Nutr. 2001; 131(4), 12171224. DOI 10.1093/jn/131.4.1217.CrossRefGoogle ScholarPubMed
Yajnik, CS, Bandopadhyay, S, Bhalerao, A, et al. Poor in-utero growth, and reduced beta cell compensation and high fasting glucose from childhood, are harbingers of glucose intolerance in young Indians. Diabetes Care. 2021; 44(12), 27472757. DOI 10.2337/dc20-3026.CrossRefGoogle ScholarPubMed
Wong, WKM, Sorensen, AE, Joglekar, MV, Hardikar, AA, Dalgaard, LT. Non-Coding RNA in pancreas and beta-cell development. Noncoding RNA. 2018; 4(4), 41. DOI 10.3390/ncrna4040041.Google ScholarPubMed
Guay, C, Regazzi, R. Circulating microRNAs as novel biomarkers for diabetes mellitus. Nat Rev Endocrinol. 2013; 9(9), 513521. DOI 10.1038/nrendo.2013.86.CrossRefGoogle ScholarPubMed
Taylor, CJ, Satoor, SN, Ranjan, AK, Pereira e Cotta, MV, Joglekar, MV. A protocol for measurement of noncoding RNA in human serum. Exp Diabetes Res. 2012; 2012, 168368. DOI 10.1155/2012/168368.CrossRefGoogle ScholarPubMed
Joglekar, MV, Wong, WKM, Ema, FK, et al. Postpartum circulating microRNA enhances prediction of future type 2 diabetes in women with previous gestational diabetes. Diabetologia. 2021; 64(7), 15161526. DOI 10.1007/s00125-021-05429-z.CrossRefGoogle ScholarPubMed
Farr, RJ, Januszewski, AS, Joglekar, MV, et al. A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy. Sci Rep. 2015; 5(1), 10375. DOI 10.1038/srep10375.CrossRefGoogle ScholarPubMed
Mestdagh, P, Van Vlierberghe, P, De Weer, A, et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009; 10(6), R64. DOI 10.1186/gb-2009-10-6-r64.CrossRefGoogle ScholarPubMed
Hardikar, AA, Farr, RJ, Joglekar, MV. Circulating microRNAs: understanding the limits for quantitative measurement by real-time PCR. J Am Heart Assoc. 2014; 3(1), e000792. DOI 10.1161/JAHA.113.000792.CrossRefGoogle ScholarPubMed
Shihana, F, Joglekar, MV, Raubenheimer, J, et al. Circulating human microRNA biomarkers of oxalic acid-induced acute kidney injury. Arch Toxicol. 2020; 94(5), 17251737. DOI 10.1007/s00204-020-02679-5.CrossRefGoogle ScholarPubMed
Olejnik, S, Algina, J. Generalized eta and omega squared statistics: measures of effect size for some common research designs. Psychol Methods. 2003; 8(4), 434447. DOI 10.1037/1082-989X.8.4.434.CrossRefGoogle ScholarPubMed
Wong, WKM, Joglekar, MV, Saini, V, et al. Machine learning workflows identify a microRNA signature of insulin transcription in human tissues. iScience. 2021; 24(4), 102379. DOI 10.1016/j.isci.2021.102379.CrossRefGoogle ScholarPubMed
Feizi, A, Meamar, R, Eslamian, M, et al. Area under the curve during OGTT in first-degree relatives of diabetic patients as an efficient indicator of future risk of type 2 diabetes and prediabetes. Clin Endocrinol (Oxf). 2017; 87(6), 696705. DOI 10.1111/cen.13443.CrossRefGoogle ScholarPubMed
Vaishya, S, Sarwade, RD, Seshadri, V. MicroRNA, proteins, and metabolites as novel biomarkers for prediabetes, diabetes, and related complications. Front Endocrinol (Lausanne). 2018; 9, 180. DOI 10.3389/fendo.2018.00180.CrossRefGoogle ScholarPubMed
Zhou, W, Sailani, MR, Contrepois, K, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Cah Rev The. 2019; 569(7758), 663671. DOI 10.1038/s41586-019-1236-x.Google ScholarPubMed
Guasch-Ferre, M, Hruby, A, Toledo, E, et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 2016; 39(5), 833846. DOI 10.2337/dc15-2251.CrossRefGoogle ScholarPubMed
Weale, CJ, Matshazi, DM, Davids, SFG, et al. MicroRNAs-126-3p and -30e-3p as potential diagnostic biomarkers for prediabetes. Diagnostics (Basel). 2021; 11(6), 949. DOI 10.3390/diagnostics11060949.CrossRefGoogle ScholarPubMed
Parrizas, M, Mundet, X, Castano, C, et al. C, etal, miR-10b and miR-223-3p in serum microvesicles signal progression from prediabetes to type 2 diabetes. J Endocrinol Invest. 2020; 43(4), 451459. DOI 10.1007/s40618-019-01129-z.CrossRefGoogle Scholar
Nunez Lopez, YO, Garufi, G, Seyhan, AA. Altered levels of circulating cytokines and microRNAs in lean and obese individuals with prediabetes and type 2 diabetes. Mol Biosyst. 2016; 13(1), 106121. DOI 10.1039/c6mb00596a.CrossRefGoogle ScholarPubMed
Parrizas, M, Brugnara, L, Esteban, Y, et al. Circulating miR-192 and miR-193b are markers of prediabetes and are modulated by an exercise intervention. J Clin Endocrinol Metab. 2015; 100(3), E407E415. DOI 10.1210/jc.2014-2574.CrossRefGoogle ScholarPubMed
Lee, JH, Lee, JH, Rane, SG. TGF-beta signaling in pancreatic islet beta cell development and function. Endocrinology. 2021; 162, bqaa233. DOI 10.1210/endocr/bqaa233.CrossRefGoogle ScholarPubMed
Hong, SH, Kang, M, Lee, KS, Yu, K. High fat diet-induced TGF-beta/Gbb signaling provokes insulin resistance through the tribbles expression. Sci Rep. 2016; 6(1), 30265. DOI 10.1038/srep30265.CrossRefGoogle ScholarPubMed
Herder, C, Zierer, A, Koenig, W, et al. Transforming growth factor-beta1 and incident type 2 diabetes: results from the MONICA/KORA case-cohort study, 1984-2002. Diabetes Care. 2009; 32(10), 19211923. DOI 10.2337/dc09-0476.CrossRefGoogle ScholarPubMed
Ameling, S, Kacprowski, T, Chilukoti, RK, et al. Associations of circulating plasma microRNAs with age, body mass index and sex in a population-based study. BMC Med Genomics. 2015; 8(1), 61. DOI 10.1186/s12920-015-0136-7.CrossRefGoogle ScholarPubMed
Supplementary material: File

Joglekar et al. supplementary material

Joglekar et al. supplementary material
Download Joglekar et al. supplementary material(File)
File 269 KB

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Circulating microRNAs from early childhood and adolescence are associated with pre-diabetes at 18 years of age in women from the PMNS cohort
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Circulating microRNAs from early childhood and adolescence are associated with pre-diabetes at 18 years of age in women from the PMNS cohort
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Circulating microRNAs from early childhood and adolescence are associated with pre-diabetes at 18 years of age in women from the PMNS cohort
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *