Multi-fluid, multi-omics signatures of insulin resistance and incident type 2 diabetes among Puerto Rican adults
Plasma proteomic signatures provided the strongest T2D prediction. Adding saliva data did not improve predictive performance of plasma data.
Authors
- Kaumudi Joshipura (ORCID: https://orcid.org/0000-0003-1964-7579)
- Liming Liang (ORCID: https://orcid.org/0000-0001-8261-3174)
- Frank B. Hu (ORCID: https://orcid.org/0000-0002-8233-6274)
- Zicheng Wang (ORCID: https://orcid.org/0009-0000-1787-6517)
- Meir J. Stampfer (ORCID: https://orcid.org/0000-0001-8865-935X)
- Danielle E. Haslam (ORCID: https://orcid.org/0000-0003-0144-3287)
- David T. Wong (ORCID: https://orcid.org/0000-0002-0290-2206)
- Shilpa N Bhupathiraju (ORCID: https://orcid.org/0000-0003-0173-9248)
- Kyu Ha Lee (ORCID: https://orcid.org/0000-0002-5313-8375)
- Tong Xia
- Teja Lakamraju
- Saravanan Thangarajan
Institutions
- Gujarat University (IN)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- University of Connecticut (US)
- University of California, Los Angeles (US)
- Cancer Research And Biostatistics (US)
- Harvard Global Health Institute (US)
- Ahmedabad University (IN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-12-04
- DOI
- https://doi.org/10.3389/fendo.2025.1699656
- Primary Topic
- Advanced Proteomics Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00