BUFFALO, NY — September 11, 2025 — A new #research paper was #published in Volume 17, Issue 8 of Aging-US on August 8, 2025, titled “AI-driven toolset for IPF and aging research associates lung fibrosis with accelerated aging.”

In this study, researchers Fedor Galkin, Shan Chen, Alex Aliper, Alex Zhavoronkov, and Feng Ren from Insilico Medicine used artificial intelligence (AI) to investigate the similarities between idiopathic pulmonary fibrosis (IPF), a severe lung disease, and the aging process. Their findings show that IPF is not simply accelerated aging, but a distinct biological condition shaped by age-related dysfunction. This insight may lead to a new approach in how scientists and clinicians treat this complex disease.

IPF mainly affects individuals over the age of 60. It causes scarring of lung tissue, making it harder to breathe and often leading to respiratory failure. Current treatments can slow the disease but rarely stop or reverse its progression. The researchers used AI to identify shared biological features between aging and fibrosis, finding new potential targets for therapy.

The team developed a “proteomic aging clock” based on protein data from more than 55,000 participants in the UK Biobank. This AI-driven tool accurately measured biological age and found that patients with severe COVID-19, who are at increased risk for lung fibrosis, also showed signs of accelerated aging. This suggests that fibrosis leaves a detectable biological trace, supporting the use of aging clocks in studying age-related diseases.

“For aging clock training, we used the UK Biobank collection of 55319 proteomic Olink NPX profiles annotated with age and gender.”

They also developed a custom AI model, ipf-P3GPT, to compare gene activity in aging lungs versus those with IPF. Although some genes were active in both, many showed opposite behavior. In fact, more than half of the shared genes had inverse effects. This means IPF does not just speed up aging but also disrupts the body’s normal aging pathways.

The study identified unique molecular signatures that distinguish IPF from normal aging. While both involve inflammation and tissue remodeling, IPF drives more damaging changes to lung structure and repair systems. This difference could guide the development of drugs that specifically target fibrosis without affecting normal aging.

By combining AI with large-scale biological data, the study also introduces a powerful toolset for examining other age-related conditions such as liver and kidney fibrosis. These models may support personalized treatments and expand understanding of the relationships between aging and disease, opening new directions for therapy development.

DOI - https://doi.org/10.18632/aging.206295

Corresponding author - Alex Zhavoronkov - [email protected]

Abstract video - https://www.youtube.com/watch?v=24lX2lHbt7o

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Keywords - aging, IPF, generative AI, transformer, proteomics

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