| Title: | AI-driven toolset for IPF and aging research associates lung fibrosis with accelerated aging. |
| Journal: | Aging |
| Published: | 8 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40782333/ |
| DOI: | https://doi.org/10.18632/aging.206295 |
| URL: | https://www.aging-us.com/article/206295/pdf |
Publication 18808
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Idiopathic pulmonary fibrosis (IPF) is a condition predominantly affecting the elderly and leading to a decline in lung function. Our study investigates the aging-related mechanisms in IPF using artificial intelligence (AI) approaches. We developed a pathway-aware proteomic aging clock using UK Biobank data and applied it alongside a specialized version of Precious3GPT (ipf-P3GPT) to demonstrate an AI-driven mode of IPF research. The aging clock shows great performance in cross-validation (R2=0.84) and its utility is validated in an independent dataset to show that severe cases of COVID-19 are associated with an increased aging rate. Computational analysis using ipf-P3GPT revealed distinct but overlapping molecular signatures between aging and IPF, suggesting that IPF represents a dysregulation rather than mere acceleration of normal aging processes. Our findings establish novel connections between aging biology and IPF pathogenesis while demonstrating the potential of AI-guided approaches in therapeutic development for age-related diseases.</p>
12 Keywords
- Aged
- Aging
- Aging, Premature
- Artificial Intelligence
- COVID-19
- Female
- Humans
- Idiopathic Pulmonary Fibrosis
- Male
- Middle Aged
- Proteomics
- SARS-CoV-2
5 Authors
- Fedor Galkin
- Shan Chen
- Alex Aliper
- Alex Zhavoronkov
- Feng Ren
Enabling scientific discoveries that improve human health