| Title: | GlycA as an Effect Modifier of Protein-Mortality Associations: A Prospective Cohort Study |
| Journal: | Clinical and Translational Science |
| Published: | 21 Apr 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42012021/ |
| DOI: | https://doi.org/10.1111/cts.70542 |
Publication 17903
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Abstract
The heterogeneity in health effects of circulating proteins remained unclear. Previous studies have identified that glycoprotein acetyls (GlycA), a stable biomarker of inflammation, were associated with risks of mortality and chronic diseases. However, it remained unclear whether the health risks of proteins may differ across people with varied GlycA levels. Based on the multi-omics profiling of the UK Biobank prospective cohort, we evaluated whether GlycA statistically modifies the protein-mortality associations. In the discovery dataset (n = 24,134), we observed that GlycA significantly modified the associations of ANG, CRHBP, CXCL16, and PRRT3 with all-cause mortality, and these findings were replicated in the validation dataset (n = 6081). Subgroup analyses further indicated that associations between these proteins and mortality can be modulated by GlycA levels. Through exploratory analyses focused on chronic diseases and cause-specific mortality, we identified that cross-products of GlycA with these proteins can be associated with cancer mortality and heart failure. Together, the findings will expand our understanding of heterogeneity in protein-health associations and highlight several potential therapeutic targets for interventions across people with varied inflammation status.</p>
15 Keywords
- Aged
- Biomarkers
- Cause of Death
- Chronic Disease
- Female
- Glycoproteins
- Heart Failure
- Humans
- Inflammation
- Male
- Middle Aged
- Neoplasms
- Prospective Studies
- Risk Factors
- United Kingdom
11 Authors
- Xinru Wang
- Ziyan Qiao
- Chengzhe Tao
- Sijing Liao
- Zhi Li
- Qiaoqiao Xu
- Yun Fan
- Yufeng Wang
- Yan Han
- Xiuliang Dai
- Chuncheng Lu
1 Application
| Application ID | Title |
|---|---|
| 70522 | Application of genetic, environment and lifestyle data to understand causal risk factors of complex metabolic alterations and develop novel prediction model |
Enabling scientific discoveries that improve human health