ArticleAging2025
A natural language processing-driven map of the aging research landscape.
Article in Aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aging research has advanced significantly over the past century, from early studies on animal models to a current emphasis on clinical and translational applications. As research literature expands exponentially, traditional narrative reviews can no longer capture the field's complexity, highlighting the need for new, unbiased synthesis tools. Here, we leverage advanced natural language processing (NLP) and machine learning (ML) techniques to analyze 461,789 abstracts related to aging published between 1925 and 2023. 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. Our results show a clear shift: early decades focused on cellular and molecular mechanisms, while recent years emphasize clinical studies, especially neurodegenerative disorders. Notably, we identify a persistent divide between the biology of aging (BoA) and clinical research, with minimal conceptual overlap between them. Furthermore, we identify distinct clusters representing key biological processes, some of which may have previously been overlooked as cohesive research domains. Finally, we highlight both established and underexplored interconnections that could guide future research. This study outlines shifting priorities and translational gaps in aging research and offers a scalable, data-driven alternative to conventional reviews.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.