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Applicability of a microRNA-based Dynamic Risk Score (DRS) for type 1 diabetes

Western Sydney University
Hardikar, Hrishkesh P ; Thorat, Vinod ; Kunte, Pooja S ; Kulkarni, Reshmi A ; Pant, Aniruddha ; Wong, Wilson ; Joglekar, Mugdha ; Hardikar, Anand
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.24433/CO.4476520.v1&rft.title=Applicability of a microRNA-based Dynamic Risk Score (DRS) for type 1 diabetes&rft.identifier=10.24433/CO.4476520.v1&rft.publisher=CodeOcean&rft.description=Identifying biomarkers of functional β-cell loss is critical in risk stratification for Type 1 Diabetes (T1D). We report a microRNA-based dynamic (responsive to environment) risk score developed using multi-center, multi-ethnic/country (“multi-context”) cohorts. Discovery (wet-lab and dry-lab) analysis identified 50 microRNAs that were measured across n=2,204 individuals from four contexts (4C=AUS/Australia, DNK/Denmark, HKG/Hong Kong SAR China, IND/India). A 4-context, microRNA-based dynamic risk score (DRS4C) was generated, which effectively stratified individuals with/without T1D. Generative artificial intelligence (GAI) was used to create an enhanced (e)DRS4C, that offered high AUC (0.84) on an independent multi-context Validation-set (n=662) and most accurately predicted future exogenous-insulin requirement at one-hour of islet transplantation in Canada (CAN) recipients. In a clinical trial assessing an emerging T1D therapy, baseline microRNA signature, but not the clinical characteristics, stratified 1-year response to Imatinib. This study harnessed ML and GAI approaches, identifying and validating a microRNA-based DRS for T1D stratification and treatment efficacy prediction. This capsule presents the code for stratifying controls and T1D study participants as well as for predicting outcomes of T1D therapy.&rft.creator=Hardikar, Hrishkesh P &rft.creator=Thorat, Vinod &rft.creator=Kunte, Pooja S &rft.creator=Kulkarni, Reshmi A &rft.creator=Pant, Aniruddha &rft.creator=Wong, Wilson &rft.creator=Joglekar, Mugdha &rft.creator=Hardikar, Anand &rft.date=2025&rft.relation=https://doi.org/10.1038/s41591-025-03730-7&rft.coverage=&rft_rights=Copyright Western Sydney University&rft_rights=CC BY-NC-ND 4.0: Attribution-Noncommercial-No Derivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0&rft_subject=Adolescent&rft_subject=Adult&rft_subject=Artificial Intelligence&rft_subject=Australia&rft_subject=Child&rft_subject=Denmark&rft_subject=Diabetes Mellitus Type 1&rft_subject=Female&rft_subject=Hong Kong&rft_subject=Humans&rft_subject=India&rft_subject=Insulin&rft_subject=Insulin-Secreting Cells&rft_subject=Islets of Langerhans Transplantation&rft_subject=Machine Learning&rft_subject=Male&rft_subject=MicroRNAs&rft_subject=Risk Assessment&rft_subject=Risk Factors&rft.type=dataset&rft.language=English Access the data
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Identifying biomarkers of functional β-cell loss is critical in risk stratification for Type 1 Diabetes (T1D). We report a microRNA-based dynamic (responsive to environment) risk score developed using multi-center, multi-ethnic/country (“multi-context”) cohorts. Discovery (wet-lab and dry-lab) analysis identified 50 microRNAs that were measured across n=2,204 individuals from four contexts (4C=AUS/Australia, DNK/Denmark, HKG/Hong Kong SAR China, IND/India). A 4-context, microRNA-based dynamic risk score (DRS4C) was generated, which effectively stratified individuals with/without T1D. Generative artificial intelligence (GAI) was used to create an enhanced (e)DRS4C, that offered high AUC (0.84) on an independent multi-context Validation-set (n=662) and most accurately predicted future exogenous-insulin requirement at one-hour of islet transplantation in Canada (CAN) recipients. In a clinical trial assessing an emerging T1D therapy, baseline microRNA signature, but not the clinical characteristics, stratified 1-year response to Imatinib. This study harnessed ML and GAI approaches, identifying and validating a microRNA-based DRS for T1D stratification and treatment efficacy prediction. This capsule presents the code for stratifying controls and T1D study participants as well as for predicting outcomes of T1D therapy.

Created: 2025-06-11

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Adolescent | Adult | Artificial Intelligence | Australia | Child | Denmark | Diabetes Mellitus Type 1 | Female | Hong Kong | Humans | India | Insulin | Insulin-Secreting Cells | Islets of Langerhans Transplantation | Machine Learning | Male | MicroRNAs | Risk Assessment | Risk Factors |

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Identifiers
  • DOI : 10.24433/CO.4476520.V1
  • Local : research-data.westernsydney.edu.au/published/ba7cb3609cdf11f0bf5f55cf520dc9e9
ACN 633 798 857