MétaCan
Menu
← Back to cohort

Metformin and the Incidence of Lymphoid Malignancies in Patients with Type 2 Diabetes

2017· article· en· W2781920357 on OpenAlexaff
Adi J. Klil‐Drori, Hui Yin, Michaël Pollak, Laurent Azoulay

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsMedicineMetforminInternal medicineHazard ratioPopulationType 2 diabetesCohortProportional hazards modelCumulative incidenceDiabetes mellitusCancerIncidence (geometry)OncologyConfidence intervalInsulinEndocrinology

Abstract

fetched live from OpenAlex

Abstract Purpose: Metformin, a first-line antidiabetic drug in type 2 diabetes, is being evaluated for prevention and treatment of various solid tumors. Metformin has also been shown to act against lymphoid malignancies (LM) in preclinical models, although population-based data are lacking. Thus, we examined whether metformin use is associated with reduced risk of LM in patients with type 2 diabetes. Patients and Methods: This was a population-based cohort study using data from the United Kingdom Clinical Practice Research Datalink. We included patients > 18 years of age newly-treated with non-insulin antidiabetic drugs between 1998 and 2014. Time-dependent Cox proportional hazards models were used to estimate hazard ratios (HR) with 95% confidence intervals (CI) of LM (inpatient diagnoses of non-Hodgkin and Hodgkin lymphoma, chronic and acute lymphocytic leukemia, and multiple myeloma) comparing the use of metformin with the use of other antidiabetic drugs, and lagged by one year for cancer latency. Secondary analyses included stratifications of metformin use by cumulative duration and dose, as well as of LM by disease type. All estimates were adjusted to ethnicity, socioeconomic status, time-updated use of other antidiabetic drugs, known risk factors (autoimmune diseases and solid organ transplant), and important comorbidities. Results: The cohort included a total of 100,091 patients, which generated 502 LM events during 500,774 person-years of follow-up (rate, 1/1000 person-years). Overall, metformin use was not associated with the incidence of LM (adjusted HR, 0.86; 95% CI, 0.63-1.16). Furthermore, there was no significant trend for duration (P=0.46) or cumulative dose (P= 0.60) of metformin use with LM incidence, with the highest categories being > 5 years and > 2,492 grams (Table 1). When stratified by disease type, metformin use was not associated with the incidence of multiple myeloma, chronic lymphocytic leukemia acute, and non-Hodgkin lymphoma (Table 2). Similar findings were seen for acute lymphoblastic leukemia (adjusted HR, 0.36; 95% CI, 0.04-3.41), and Hodgkin lymphoma (adjusted HR, 0.94; 95% CI, 0.23-3.91). Conclusions To our knowledge, this is the first population-based study to examine the association between metformin use and LM incidence. We observed no duration- or dose-response of metformin with LM incidence, which are paramount in the concept of chemoprevention. There were no important differences between LM types, although precision for acute lymphoblastic leukemia and Hodgkin lymphoma was limited due to sample size. While our findings are observational and apply to the diabetic population, our findings provide no rationale for the prospective investigation of metformin to prevent LM. Download : Download high-res image (370KB) Download : Download full-size image Disclosures No relevant conflicts of interest to declare.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicMetabolism, Diabetes, and Cancer→French-language works237,207→