Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping
Authors' conclusion
By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.
Study context
- Population
- UK Biobank participants who underwent MRI (liver, pancreas, heart and kidney T1 time)UK Biobank
- Participants
- 43,881 participants
- Exposure
- High vs low burden of fibrosis
- Outcome
- Independent genetic loci associated with organ T1 time
Methodological detailsmachine learning, phenome-wide association analyses, genome-wide association analyses
- Machine learning
- Phenome-wide association analyses
- Genome-wide association analyses
Main finding
As reported in the abstract
Liver T1 time loci
27
Pancreas T1 time loci
18
Myocardial T1 time loci
11
Renal cortex T1 time loci
10
Observational data: associations, not effects.
Further findings
| Outcome | HR (95% CI) | P value | |
|---|---|---|---|
| Mortality by number of organs with T1 time in top quintileMultivariable-adjusted · multivariable-adjusted analysis | 3.31 (1.77–6.19) | P = 1.78 × 10-4 | |
| 0.10.31310Hazard ratio (log scale) |
Mortality by number of organs with T1 time in top quintile
- Increasing number of organs with T1 time in top quintile associated with increased mortality
Original publication
Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping. Nat Med. Published 28 May 2024. doi:10.
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