AI tracks nearly 100 years of aging research, revealing key trends and gaps
“This study outlines shifting priorities and translational gaps in aging research and offers a scalable, data-driven alternative to conventional reviews.”
2025-12-23
(Press-News.org)
“This study outlines shifting priorities and translational gaps in aging research and offers a scalable, data-driven alternative to conventional reviews.”
BUFFALO, NY — December 23, 2025 — A new research paper was published in Volume 17, Issue 11 of Aging-US on November 25, 2025, titled “A natural language processing–driven map of the aging research landscape.”
In this study, Jose Perez-Maletzki from Universidad Europea de Valencia and Universitat de València, together with Jorge Sanz-Ros from Stanford University School of Medicine, used artificial intelligence (AI) to analyze a century of global aging research, revealing shifts in focus and highlighting underexplored areas.
The team analyzed over 460,000 scientific abstracts published between 1925 and 2023 to identify key themes, trends, and research gaps in the study of aging. Their goal was to provide a comprehensive, unbiased view of how the field has evolved and where future research could have the greatest impact.
The study found that aging research has moved from basic cellular studies and animal models to a growing focus on clinical topics, particularly age-related diseases such as Alzheimer’s and dementia. Using natural language processing and machine learning, the researchers grouped publications into thematic clusters and tracked how interest in each topic changed over time.
“By integrating Latent Dirichlet Allocation (LDA), term frequency-inverse document frequency (TF-IDF) analysis, dimensionality reduction and clustering, we delineate a comprehensive thematic landscape of aging research.”
One key finding was the growing separation between basic biological studies and clinical research. While both areas have grown significantly, they often progress independently with limited overlap. Clinical studies tend to focus on geriatrics, healthcare, and neurodegenerative diseases, while basic science emphasizes cellular mechanisms such as oxidative stress, telomere shortening, mitochondrial dysfunction, and senescence. The authors note that this lack of integration limits the translation of laboratory discoveries into medical applications.
The study also showed that some emerging topics, such as autophagy, RNA biology, and nutrient sensing, are expanding rapidly but remain separated from clinical applications. In contrast, long-established links, such as those between cancer and aging, remain strong. The analysis also highlighted that potentially important associations, such as those between mitochondrial dysfunction and senescence or epigenetics and autophagy, are rarely studied and may be new research opportunities.
This AI-driven analysis offers a new way to guide future research by identifying how different areas of aging science are interconnected or isolated. It also highlights how research priorities may be shaped by policy or funding trends, as seen in the heavy focus on Alzheimer’s disease.
As the global population continues to age, understanding how biological processes relate to clinical outcomes is critical. This study not only offers a historical map of aging science but also serves as a tool to support more connected, interdisciplinary, and effective future research.
Paper DOI: https://doi.org/10.18632/aging.206340
Corresponding author: Jorge Sanz-Ros – jsanzros@stanford.edu
Abstract video: https://www.youtube.com/watch?v=O4dJUGQ2ZcU
Keywords: aging, literature mining, natural language processing, topic modelling, synthesis
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[Press-News.org] AI tracks nearly 100 years of aging research, revealing key trends and gaps
“This study outlines shifting priorities and translational gaps in aging research and offers a scalable, data-driven alternative to conventional reviews.”
