Improved sex-specific cardiovascular risk prediction with multi-omics data in people with type 2 diabetes
(2026) In Cardiovascular Diabetology 25(1).- Abstract
Background: To evaluate whether integrating proteomics, metabolomics, and a cardiovascular disease specific polygenic risk score (CVD-PRS) in the SCORE2-Diabetes model improves sex-specific 10-year prediction of major adverse cardiovascular events (MACE) in individuals with type 2 diabetes (T2D). Methods: Genome-wide association study (GWAS), plasma proteomics (with the Olink Explore 3072 platform), and metabolomics (with nuclear magnetic resonance spectroscopy by Nightingale Health) data were measured in the UK Biobank. A novel sex-specific protein algorithm was developed using bootstrap-LASSO (Least absolute shrinkage and selection operator) regression. The CVD-PRS and sex-specific metabolite algorithms were used from previous UK... (More)
Background: To evaluate whether integrating proteomics, metabolomics, and a cardiovascular disease specific polygenic risk score (CVD-PRS) in the SCORE2-Diabetes model improves sex-specific 10-year prediction of major adverse cardiovascular events (MACE) in individuals with type 2 diabetes (T2D). Methods: Genome-wide association study (GWAS), plasma proteomics (with the Olink Explore 3072 platform), and metabolomics (with nuclear magnetic resonance spectroscopy by Nightingale Health) data were measured in the UK Biobank. A novel sex-specific protein algorithm was developed using bootstrap-LASSO (Least absolute shrinkage and selection operator) regression. The CVD-PRS and sex-specific metabolite algorithms were used from previous UK Biobank projects. In a subset of 990 participants with T2D, age 40–69 years, with no prior MACE, and complete multi-omics data, we evaluated, which omics data improved the SCORE2-Diabetes model performance using Harrell’s C-index. Results: Overall 9 proteins were selected for males and 7 for females and adding them to the SCORE2-Diabetes model significantly improved discrimination in the total population (C-index increase from 0.766 to 0.835 (P < 0.001)). Further adding of metabolites significantly improved model performance (C-index, 0.846, P = 0.035), which was mostly attributable to model improvement among males (∆C-index, 0.012, P = 0.078) but not among females (∆C-index, 0.004, P = 0.723). Further adding the CVD-PRS did not statistically significantly improve the SCORE2-Diabetes + proteomics + metabolomics model further in the total population (C-index, 0.848 (P = 0.070)). Conclusions: Sex-specific proteomic signatures markedly improved 10-year MACE risk prediction in individuals with T2D. In men but not in women, further integration of metabolomics may enhance model performance whereas adding the CVD-PRS is not needed. External validation is warranted.
(Less)
- author
- Xie, Ruijie ; Herder, Christian ; Sha, Sha ; Brenner, Hermann ; Carlsson, Sigrid LU and Schöttker, Ben
- organization
- publishing date
- 2026-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cardiovascular risk, Multi-omics, Proteomics, SCORE2-diabetes, Sex-specific, Type 2 diabetes
- in
- Cardiovascular Diabetology
- volume
- 25
- issue
- 1
- article number
- 25
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:105028753860
- pmid:41444601
- ISSN
- 1475-2840
- DOI
- 10.1186/s12933-025-03036-5
- language
- English
- LU publication?
- yes
- id
- 95175785-a9de-4b9c-9a8a-93d9fa3935c1
- date added to LUP
- 2026-02-17 13:12:36
- date last changed
- 2026-02-18 03:00:11
@article{95175785-a9de-4b9c-9a8a-93d9fa3935c1,
abstract = {{<p>Background: To evaluate whether integrating proteomics, metabolomics, and a cardiovascular disease specific polygenic risk score (CVD-PRS) in the SCORE2-Diabetes model improves sex-specific 10-year prediction of major adverse cardiovascular events (MACE) in individuals with type 2 diabetes (T2D). Methods: Genome-wide association study (GWAS), plasma proteomics (with the Olink Explore 3072 platform), and metabolomics (with nuclear magnetic resonance spectroscopy by Nightingale Health) data were measured in the UK Biobank. A novel sex-specific protein algorithm was developed using bootstrap-LASSO (Least absolute shrinkage and selection operator) regression. The CVD-PRS and sex-specific metabolite algorithms were used from previous UK Biobank projects. In a subset of 990 participants with T2D, age 40–69 years, with no prior MACE, and complete multi-omics data, we evaluated, which omics data improved the SCORE2-Diabetes model performance using Harrell’s C-index. Results: Overall 9 proteins were selected for males and 7 for females and adding them to the SCORE2-Diabetes model significantly improved discrimination in the total population (C-index increase from 0.766 to 0.835 (P < 0.001)). Further adding of metabolites significantly improved model performance (C-index, 0.846, P = 0.035), which was mostly attributable to model improvement among males (∆C-index, 0.012, P = 0.078) but not among females (∆C-index, 0.004, P = 0.723). Further adding the CVD-PRS did not statistically significantly improve the SCORE2-Diabetes + proteomics + metabolomics model further in the total population (C-index, 0.848 (P = 0.070)). Conclusions: Sex-specific proteomic signatures markedly improved 10-year MACE risk prediction in individuals with T2D. In men but not in women, further integration of metabolomics may enhance model performance whereas adding the CVD-PRS is not needed. External validation is warranted.</p>}},
author = {{Xie, Ruijie and Herder, Christian and Sha, Sha and Brenner, Hermann and Carlsson, Sigrid and Schöttker, Ben}},
issn = {{1475-2840}},
keywords = {{Cardiovascular risk; Multi-omics; Proteomics; SCORE2-diabetes; Sex-specific; Type 2 diabetes}},
language = {{eng}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{Cardiovascular Diabetology}},
title = {{Improved sex-specific cardiovascular risk prediction with multi-omics data in people with type 2 diabetes}},
url = {{http://dx.doi.org/10.1186/s12933-025-03036-5}},
doi = {{10.1186/s12933-025-03036-5}},
volume = {{25}},
year = {{2026}},
}