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Record W4411730241 · doi:10.1016/j.arr.2025.102817

Emerging uncertainty on the anti-aging potential of metformin

2025· review· en· W4411730241 on OpenAlexaff
Matthew Thomas Keys, Jesper Hallas, Richard A. Miller, Samy Suissa, Kaare Christensen

Bibliographic record

VenueAgeing Research Reviews · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsMcGill University
FundersVelux FondenVelux Stiftung
KeywordsMetforminMedicineObservational studyDiabetes mellitusType 2 diabetesAgeingPopulation ageingType 2 Diabetes MellitusPopulationClinical trialBioinformaticsIntensive care medicinePharmacologyInternal medicineBiologyEndocrinologyEnvironmental health

Abstract

fetched live from OpenAlex

Metformin is the most commonly prescribed glucose-lowering agent worldwide for the treatment of type II diabetes. Due to evidence of improvements in healthspan and lifespan in model organisms, and mechanistic data relevant to the hallmarks of aging, it has been considered a promising candidate in the search for pharmacological interventions that may attenuate the ageing process in humans. Various epidemiological studies have been influential in generating support for this hypothesis. These include pronounced anticancer and cardioprotective benefits compared to other antidiabetic treatments, and an observation of metformin use in type II diabetes being associated with better survival than that of the general population. Here we discuss recent developments in the evidence underlying the rationale for using metformin to target ageing. We describe the methodological limitations of some of the early and most influential findings and critically assess their scientific follow-up, including replication attempts of key experimental and observational findings, and a range of clinical trials of metformin in individuals without type II diabetes. These developments generally illustrate an emerging uncertainty in the anti-aging potential of metformin.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.099
GPT teacher head0.437
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2025
Admission routes1
Has abstractyes

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