Biological ageing-related plasma proteomics improves chronic liver disease prediction: a prospective UK Biobank study
BMC Gastroenterology, 2026
Ding J., Zhou Y., Liu Y., Dai C., Zeng B., Lin B., Hu J.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Hepatology Aging | Patient Stratification | Plasma |  Olink Explore 3072/384 |
Abstract
Background
Biological ageing may predict chronic liver disease better than chronological age, but its value alongside genetic susceptibility and circulating proteomics is unclear. We assessed ageing acceleration and developed an integrated prediction framework.
Methods
This prospective UK Biobank study included 325,580 participants without baseline liver-related disease. BioAgeAccel and PhenoAgeAccel were calculated, polygenic risk scores (PRSs) were built for metabolic dysfunction-associated steatotic liver disease (MASLD) and cirrhosis, and Olink proteomics derived protein scores. Cox models, spline analyses, PRS-stratified analyses, and time-dependent receiver operating characteristic analyses were performed.
Results
During follow-up, 9,876 incident liver disease events occurred, including 4,513 MASLD, 1,521 cirrhosis, 258 liver cancers, and 651 liver-specific deaths. Both ageing metrics were independently associated with all outcomes, with stronger effects for PhenoAgeAccel on severe endpoints. In the highest versus lowest group, hazard ratios for PhenoAgeAccel were 2.01 for overall liver disease, 4.19 for cirrhosis, 4.62 for liver cancer, and 3.98 for liver-specific mortality; corresponding BioAgeAccel estimates were 1.54, 2.34, 1.85, and 1.89. Proteomic signatures for MASLD and cirrhosis showed limited overlap and immune-inflammatory enrichment. Protein scores improved discrimination more than PRS alone; for 3-year MASLD, the area under the curve increased to 0.858 with protein-score addition versus 0.827 with PRS addition, and the full model reached 0.870.
Conclusions
Accelerated biological ageing independently predicts chronic liver disease. The main innovation is a multi-omics framework integrating ageing acceleration, PRS, and proteomic scores, demonstrating that protein-level information adds more predictive value than genetic risk alone for early risk stratification, especially for MASLD and cirrhosis.