Evidence map›Paper›PMID 40473952›Full record

ArticleNature medicine2025

A microRNA-based dynamic risk score for type 1 diabetes.

Mugdha V Joglekar, Wilson K M Wong, Pooja S Kunte, Hrishikesh P Hardikar, Reshmi A Kulkarni, Ikhlak Ahmed, Ryan J Farr, Nhan Ho Trong Pham, Madilyn Coles, Simranjeet Kaur and 20 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

30 authors.

Mugdha V Joglekar *Diabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia. m.joglekar@westernsydney.edu.au.ORCID http://orcid.org/0000-0001-5346-2266
Wilson K M Wong *Diabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Pooja S Kunte *Diabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Hrishikesh P Hardikar *Diabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Reshmi A KulkarniDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.ORCID http://orcid.org/0009-0000-9156-0617
Ikhlak AhmedSidra Medical Research Centre, Doha, Qatar.ORCID http://orcid.org/0000-0002-5753-9627
Ryan J FarrDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0003-1636-1815
Nhan Ho Trong PhamDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Madilyn ColesDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Simranjeet KaurSteno Diabetes Center Copenhagen, Herlev, Denmark.ORCID http://orcid.org/0000-0003-0490-1028
Cody L MaynardDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Riley HaywardDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Vinod ThoratHealthcare Analytics, AlgoAnalytics, Pune, India.
Aniruddha PantHealthcare Analytics, AlgoAnalytics, Pune, India.
Ammira A AkilSidra Medical Research Centre, Doha, Qatar.
Kim C DonaghueInstitute of Endocrinology and Diabetes, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Alicia J JenkinsThe University of Sydney, Faculty of Medicine and Health, Camperdown, New South Wales, Australia.
Milan K PiyaDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Maria E CraigInstitute of Endocrinology and Diabetes, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0001-6004-576X
William M HagueDiscipline of Obstetrics & Gynecology and Robinson Research Institute, University of Adelaide, Adelaide, South Australia, Australia.
Chittaranjan S YajnikDiabetes Unit, KEM Hospital & Research Centre, Pune, India.ORCID http://orcid.org/0000-0002-2911-2378
Juliana C N ChanDepartment of Medicine and Therapeutics, and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.ORCID http://orcid.org/0000-0003-1325-1194
A M James ShapiroClinical Islet Transplant Program, University of Alberta, Edmonton, Alberta, Canada.
Elizabeth A DavisTelethon Kids Institute, Northern Entrance, Perth Children's Hospital, Nedlands, Western Australia, Australia.
Timothy W JonesTelethon Kids Institute, Northern Entrance, Perth Children's Hospital, Nedlands, Western Australia, Australia.
Stephen E GitelmanUniversity of California San Francisco, San Francisco, CA, USA.
Ronald C W MaDepartment of Medicine and Therapeutics, and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.ORCID http://orcid.org/0000-0002-1227-803X
Flemming PociotSteno Diabetes Center Copenhagen, Herlev, Denmark.
Anandwardhan A HardikarDiabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia. a.hardikar@westernsydney.edu.au.ORCID http://orcid.org/0000-0001-5587-2090
PREDICT T1D Study Group

Funding

Department of Health | National Health and Medical Research Council (NHMRC) 2021/GNT2011557JDRF 3-APF-2016-178-A-N and 1-FAC-2021-1063-A-NJDRF 3-PDF-2023-1324-A-NJuvenile Diabetes Research Foundation Australia (JDRF Australia) 4-CDA-2016-228-M-B, 1-CDA-2021-1084-M-BLeona M. and Harry B. Helmsley Charitable Trust (Helmsley Charitable Trust) 3-SRA-2019-694-M-B and 1-SRA2021-1067-MB
6 · The paper itself

Abstract

Identifying individuals at high risk of type 1 diabetes (T1D) is crucial as disease-delaying medications are available. Here we report a microRNA (miRNA)-based dynamic (responsive to the environment) risk score developed using multicenter, multiethnic and multicountry ('multicontext') cohorts for T1D risk stratification. Discovery (wet and dry lab) analysis identified 50 miRNAs associated with functional β cell loss, which is a hallmark of T1D. These miRNAs measured across n = 2,204 individuals from four contexts (4C: Australia, Denmark, Hong Kong SAR People's Republic of China, India) led to a four-context, miRNA-based dynamic risk score (DRS) that effectively stratified individuals with and without T1D. Generative artificial intelligence was used to create an enhanced four-context, miRNA-based DRS, which offered good predictive power (area under the curve = 0.84) for T1D stratification in a separate multicontext validation dataset (n = 662), and accurately predicted future exogenous insulin requirement at 1 hour of islet transplantation. In a clinical trial assessing the imatinib drug therapy, baseline miRNA signature, rather than clinical characteristics, distinguished drug responders from nonresponders at 1 year. This study harnessed machine learning/generative artificial intelligence approaches, identifying and validating a miRNA-based DRS for T1D discrimination and treatment efficacy prediction.

Indexed as

Diabetes Mellitus, Type 1MicroRNAsAdolescentAdultArtificial IntelligenceAustraliaChildDenmarkFemaleHong KongHumansIndiaInsulinInsulin-Secreting CellsIslets of Langerhans TransplantationMachine LearningInsulinMicroRNAs

Identifiers

PMID40473952
PMCPMC12353805

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.