Plasma multi-omics and machine learning reveal predictive biomarkers for type 2 diabetes and retinopathy in Qatar biobank cohort
Integrating multi-omics data with machine learning enables accurate risk stratification for T2D and DR in Middle Eastern populations. The miR-29c-PROM1 axis and associated proteins represent promising biomarkers for early detection and targeted intervention. Leveraging QBB resources, this study lays the groundwork for precision health initiatives aimed at mitigating diabetes-related complications in a high-risk Middle Eastern cohort.
Authors
- Amaresh K. Ranjan (ORCID: https://orcid.org/0000-0001-7529-7409)
- Ajaz A. Bhat (ORCID: https://orcid.org/0000-0003-3079-5097)
- Evonne C. Chin-Smith (ORCID: https://orcid.org/0000-0002-1935-2941)
- Wilson K. M. Wong (ORCID: https://orcid.org/0000-0001-7736-6834)
- Anandwardhan A. Hardikar (ORCID: https://orcid.org/0000-0001-5587-2090)
- Khalid A. Fakhro (ORCID: https://orcid.org/0000-0002-3150-1276)
- Hana Q. Sadida (ORCID: https://orcid.org/0009-0003-2932-0323)
- Sujitha Padma Jeya
- Jyothi Lakshmi
- Ammira Al‐Shabeeb Akil (ORCID: https://orcid.org/0000-0001-5381-070X)
- Mugdha V. Joglekar (ORCID: https://orcid.org/0000-0001-5346-2266)
- Ikhlak Ahmed (ORCID: https://orcid.org/0000-0002-5753-9627)
- Mya Polkamp (ORCID: https://orcid.org/0009-0002-8558-3315)
- Hrishikesh P. Hardikar
- Sura Ahmed Hussain
Institutions
- Camden and Campbelltown Hospitals (AU)
- Qatar Airways (Qatar) (QA)
- Sidra Medical and Research Center (QA)
- Hamad bin Khalifa University (QA)
- Weill Cornell Medical College in Qatar (QA)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1186/s12967-025-07113-x
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Sidra Medicine
- Fonds National de la Recherche Luxembourg
- Qatar National Research Fund