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Record W4324104107 · doi:10.1093/gerona/glad083

Geroscience for the Next Chapter of Medicine

2023· article· en· W4324104107 on OpenAlexaff
Gustavo Duque, Lewis A. Lipsitz, Luigi Ferrucci, Siobhan Addie, Stacy Carrington‐Lawrence, Ronald A. Kohanski

Bibliographic record

VenueThe Journals of Gerontology Series A · 2023
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Institutes of Health
KeywordsComputer scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

In 1863, Rudolph Virchow published “Cellular Pathology as Based upon Physiological and Pathological Histology,” establishing for the first time that cellular pathology was tightly connected with human pathology (1). Over subsequent years, the intuition of Dr. Virchow became the driver of scientific discovery in medical science, and the understanding of cell physiology and pathology expanded our knowledge of the pathophysiology of acute and chronic diseases. This understanding had fundamental roles in identifying targets for preventing, curing, and modifying human disease. Today, accumulating evidence from the field of geroscience promises to make a similar impact on medicine and public health. Curiosity about the origin of aging is probably as old as the human species. With the rising evidence that the pace of aging is malleable in animal models, research on the biological mechanisms of aging has expanded more than ever before, moving from interesting speculation to a science at the front edge of medicine. As scientists investigated the molecular and cellular mechanisms of aging biology, they discovered that the “mechanisms of aging” are, in fact, failures of homeostatic mechanisms that maintain life with attendant cellular damage and dysfunction driving deficit accumulation that manifests clinically as chronic diseases and decline of physical and cognitive functions (2). More recent discoveries of factors that preserve or restore youthful molecular and cellular processes in animal models have suggested that the pace of aging is malleable, and we can enhance healthy longevity (3). These exciting observations gave birth to the field of geroscience, where expanding the focus of aging research from the mechanisms of aging to the mechanisms of life has tremendous implications. Geroscience encompasses a life course approach because the decline of efficiency of the mechanisms that protect from damage accumulation likely starts early. The balance between damage accumulation and damage avoidance and repair may be tilted by intrinsic and extrinsic stressors or the progressive weakening of resilience strategies. This balance affects health stability before any clinical evidence of diseases emerges. It also connects current and future health to social, economic, psychological, and environmental experiences, breaking the boundaries between medical care and public health (4). Last but not least, “gerotherapeutics” target fundamental resilience strategies to improve health span and prevent or delay the onset of multiple diseases in older age. Many trials of potential therapeutic agents are currently underway. The modern concept of geroscience was developed by Felipe Sierra and Ron Kohanski in collaboration with other scientists when Dr. Sierra was still working at the National Institute on Aging (NIA) (5). NIA is fully committed to advancing progress in this important new branch of science and, in this context, is promoting the fourth Geroscience Summit, which will be held in Bethesda on April 24–26, 2023, at the Natcher Conference Center, and is aimed to review and discuss the state-of-the-art accomplishments in this field (https://www.nia.nih.gov/2023-fourth-geroscience-summit). The aims of the Summit are diverse and highly interconnected. They include: (i) understand how geroscience can become a more inclusive approach to medical care for all people and help address health disparities; (ii) explore mechanisms that, during aging, increase susceptibility to multimorbidity and geriatric syndromes; (iii) discuss conceptual development of “measures” of the pace of aging as well as characteristics and performance of currently available measures; (iv) consider the importance and utility of mathematical modeling and artificial intelligence in geroscience; (v) discuss the new design features that will be required to implement geroscience clinical trials. These topics touch on the most innovative and essential aspects of the geroscience initiative. There is strong evidence that complex biological, environmental, sociocultural, and behavioral factors affect the quality of aging and substantially influence health disparities across individuals and communities. However, key domains of exposures and biological mechanisms that mediate these associations and may be targeted for interventions are ripe for further exploration, especially if considered from a life course perspective. The geroscience hypothesis acknowledges that aging causes global susceptibility to diseases clinically emerging as multimorbidity and frailty. Thus, the study of special populations that experience accelerated trajectories of comorbid diseases or those with exceptional health at older ages (eg, centenarians) provides clues about the mechanisms of such susceptibility or resistance and may inform biological research on health disparities related to aging. The translation of these concepts to prevention and care in older patients will necessarily require the development of new research models that reflect phenotypes and clinical dimensions observed in older patients, including measures of multimorbidity and frailty. Infrastructures are needed that facilitate the rapid translation of knowledge gathered from laboratory studies to humans and, vice versa, allowing the testing of hypotheses that spring from human studies in models to validate how gerotherapeutics affect the molecular and cellular drivers of aging. The training of a new generation of geriatricians will benefit from the development of a “geroscience toolbox” that contains guidelines on selecting the appropriate interventions to improve health and quality of life for older people. Important goals are a better conceptualization and improved performance of the tools currently available for measuring “biological aging” through new mathematical models that integrate information conveyed by multiple sources and omics. The ultimate test for the geroscience hypothesis will be the success of clinical trials testing the effects of gerotherapeutics. Substantial work has been done to develop the unique features that need to be included in the design of these trials, although little practical experience has been gathered and published since most of the trials are ongoing. Geroscience is taking center stage in aging research and its implications for human health span. The Summit represents an opportunity to review what we have accomplished and discuss as a scientific community what the priorities should be for future actions. In accordance with the philosophy of this Summit, the Journals of Gerontology Series A: Biological Sciences and Medical Sciences are transforming their Translational Section into a permanent “Translational Geroscience Section,” bringing the opportunity to investigators from all disciplines interested in geroscience to share their discoveries and nurture this growing field from multiple perspectives and experimental approaches. We hope to see you as a participant at the Summit, and this journal will keep you informed on the most recent and promising developments in this field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.310
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.3100.191

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.189
GPT teacher head0.400
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractno

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