100 Unsolved Questions in Aging Science Chart a Course for Longevity Research
A landmark roadmap identifies 100 open problems in biogerontology, using AI to map research gaps and guide the next decade of aging science.
Summary
A large international team of aging researchers has compiled a curated list of 100 open questions in longevity and aging science. Gathered through community engagement and analyzed with Natural Language Processing, the problems span molecular and cellular mechanisms of aging, comparative biology, model organisms, biomarkers, and therapeutic interventions. The list distinguishes between well-established questions and emerging research gaps, offering the field a structured roadmap. All 100 questions are publicly available on a dedicated website, making this a living resource for the biogerontology community. The effort highlights how much remains unknown despite decades of progress and aims to prioritize and accelerate future discoveries in healthy aging and longevity medicine.
Detailed Summary
Aging science has made extraordinary strides over recent decades, yet the field lacks a systematic accounting of what remains unknown. Without a clear map of open questions, research efforts can cluster around familiar problems while genuinely novel and high-impact gaps go unaddressed. This paper attempts to correct that by presenting a community-sourced, AI-assisted roadmap of 100 unresolved problems in biogerontology.
The research team, drawn from leading institutions across Europe, North America, and Asia, collected candidate questions through broad community engagement with scientists working in aging biology. They then applied Natural Language Processing techniques to evaluate how frequently each question appears in the existing scientific literature, helping distinguish between questions that are well-recognized but unsolved and those that are genuinely underexplored or newly emerging.
The resulting 100 open problems are organized into thematic categories including molecular and cellular mechanisms of aging, comparative biology and model organisms, biomarkers of aging, and the development of therapeutic interventions. Both foundational long-standing questions and more recent, specific research gaps are included, giving the list relevance across career stages and subdisciplines.
The implications are significant for both basic researchers and clinicians interested in longevity medicine. By making the full list publicly accessible at longevityknowledge.app, the authors create a dynamic resource that can guide grant priorities, research agendas, and collaborative initiatives across the global aging research community.
One important caveat is that this work is based solely on the abstract and thus the precise content of the 100 questions is not fully assessable here. Additionally, question selection involved community input, which may reflect existing biases in who participates in such processes. Nonetheless, the NLP-based gap analysis adds an objective layer to counterbalance subjective curation.
Key Findings
- 100 open problems in aging science were curated via community engagement and NLP-based literature analysis.
- Questions span molecular mechanisms, comparative biology, biomarkers, and therapeutic interventions.
- NLP identified both well-established unsolved questions and emerging underexplored research gaps.
- All 100 problems are publicly available at longevityknowledge.app as a living community resource.
- The roadmap aims to prioritize and accelerate future progress across the entire biogerontology field.
Methodology
Questions were gathered through community engagement with aging researchers and subsequently analyzed using Natural Language Processing to assess their prevalence in the scientific literature. This dual approach identified both recognized unsolved problems and underexplored emerging gaps. The final curated list was categorized thematically by a large international author consortium.
Study Limitations
Only the abstract was available for analysis, meaning the specific content of the 100 questions could not be fully evaluated. Community-sourced question collection may reflect participation biases toward established research networks. As a roadmap paper, it does not itself generate new experimental data, limiting direct scientific conclusions.
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