A new research paper 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,” reveals significant shifts in the focus of aging research over the past century. Conducted by Jose Perez-Maletzki from Universidad Europea de Valencia and Universitat de València, alongside Jorge Sanz-Ros from Stanford University School of Medicine, the study employs artificial intelligence (AI) to analyze over 460,000 scientific abstracts published between 1925 and 2023, aiming to highlight key themes and gaps in the field of aging research.
The research indicates a transition away from fundamental cellular studies and animal models toward a more pronounced emphasis on clinical issues, particularly age-related diseases such as Alzheimer’s and dementia. By utilizing natural language processing techniques and machine learning, the researchers identified thematic clusters within the publications and tracked evolving interests across different topics 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,” the authors explained in the study. This methodological approach delineates a comprehensive view of aging science, allowing for a more nuanced understanding of how the discipline has transformed.
A notable finding is the growing divide between basic biological studies and clinical research. While both domains have witnessed substantial growth, they frequently advance independently, lacking significant overlap. Clinical studies primarily address geriatrics, healthcare, and neurodegenerative diseases, whereas fundamental science focuses on cellular mechanisms, including oxidative stress, telomere shortening, mitochondrial dysfunction, and senescence. This disconnect hinders the translation of laboratory advancements into clinical applications, the authors observed.
The analysis also pinpointed rapidly expanding topics, such as autophagy, RNA biology, and nutrient sensing, which remain largely unconnected to clinical applications. Conversely, established links, such as the relationship between cancer and aging, continue to be robust. Importantly, the study uncovered potentially significant associations, such as those between mitochondrial dysfunction and senescence or epigenetics and autophagy, which remain underexplored and suggest new avenues for future research.
This AI-driven analysis presents a novel framework for guiding prospective research by illuminating the interconnectedness or isolation of various areas within aging science. It underscores how research priorities can be influenced by policy or funding trends, as evidenced by the substantial focus on Alzheimer’s disease. As the global population ages, a deeper understanding of the relationship between biological processes and clinical outcomes becomes increasingly vital for effective intervention strategies.
This study not only charts the historical landscape of aging research but also serves as a strategic tool for fostering more interdisciplinary and impactful future studies. By highlighting underexplored areas and emphasizing the need for integration between basic and clinical research, it aims to catalyze advancements that can improve health outcomes in an aging population.
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Journal reference:
DOI: https://doi.org/10.18632/aging.206340
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