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Record W4407382010 · doi:10.1016/j.metabol.2025.156158

Metabolaging: a new geroscience perspective linking aging pathologies and metabolic dysfunction

2025· review· en· W4407382010 on OpenAlexafffund
Fadi Khalaf, Dalia Barayan, Sean Saldanha, Marc G. Jeschke

Bibliographic record

VenueMetabolism · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsHamilton Health SciencesPopulation Health Research Institute
FundersInstitute of AgingCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of Health
KeywordsPerspective (graphical)MedicinePsychologyNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

With age, our metabolic systems undergo significant alterations, which can lead to a cascade of adverse effects that are implicated in both metabolic disorders, such as diabetes, and in the body's ability to respond to acute stress and trauma. To elucidate the metabolic imbalances arising from aging, we introduce the concept of "metabolaging." This framework encompasses the broad spectrum of metabolic disruptions associated with the hallmarks of aging, including the functional decline of key metabolically active organs, like the adipose tissue. By examining how these organs interact with essential nutrient-sensing pathways, "metabolaging" provides a more comprehensive view of the systemic metabolic imbalances that occur with age. This concept extends to understanding how age-related metabolic disturbances can influence the response to acute stressors, like burn injuries, highlighting the interplay between metabolic dysfunction and the ability to handle severe physiological challenges. Finally, we propose potential interventions that hold promise in mitigating the effects of metabolaging and its downstream consequences.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.306
Teacher spread0.280 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2025
Admission routes2
Has abstractyes

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