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The effects of colchicine in patients with diabetes mellitus and chronic coronary artery disease: a post-hoc analysis of the LoDoCo2-trial

2022· article· en· W4306320254 on OpenAlexaff
Niekbachsh Mohammadnia, J. Los, Tjerk S.J. Opstal, Aernoud T.L. Fiolet, John W. Eikelboom, Arend Mosterd, Stefan M. Nidorf, Charley Budgeon, Jan G.P. Tijssen, Peter L. Thompson, C J Tack, S Simsek, W A Bax, Jan H. Cornel, Saloua El Messaoudi

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineInternal medicineCoronary artery diseaseColchicineDiabetes mellitusPlaceboHazard ratioMyocardial infarctionPost-hoc analysisCardiologyInflammasomeClinical endpointProportional hazards modelInflammationRandomized controlled trialEndocrinologyPathologyConfidence interval

Abstract

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Abstract Background Atherosclerosis is an inflammatory disease and is accelerated by diabetes mellitus (DM). Patients with chronic coronary artery disease (CAD) who also have DM are at high risk of recurrent cardiovascular events. The role of inflammation in atherosclerosis is well established, whereas the role of inflammation on incident and progression of DM has been hypothesized. The nucleotide-binding oligomerization domain-, leucine-rich repeat-, and pyrin domain-containing protein 3 (NLRP3) inflammasome in particular, may play an important role in the onset and progression of T2DM. The anti-inflammatory drug colchicine attenuates the NLRP3-inflammasome. The Low-Dose Colchicine 2 (LoDoCo2) trial showed that colchicine reduces cardiovascular risk in patients with chronic CAD. Purpose The purpose of this study was to assess the effects of colchicine in patients with chronic CAD and DM on cardiovascular events as well as the effect of colchicine on the development of new-onset DM. Methods The LoDoCo2 trial randomized 5522 to placebo or colchicine, with a median follow-up of 28.6 months (interquartile range 20.5–44.4). The primary endpoint was a composite of cardiovascular death, spontaneous myocardial infarction, ischaemic stroke, or ischaemia-driven revascularization. Secondary outcomes consisted of the aforementioned events, separately. Cox proportional hazards models were used to investigate univariable associations between DM status for all endpoints in the placebo group. The interactions between treatment group and DM status were evaluated with the addition of treatment and the treatment-by-DM variable interaction. Results In total, 1007 participants (18.2%) had DM at baseline. The hazard ratio for the primary endpoint was 0.87 (95% CI, 0.61–1.25) in those with DM and 0.64 (95% CI, 0.51–0.80) in those without DM (p for interaction>0.05). Treatment effects of colchicine were consistent over all secondary endpoints (p for interaction>0.05). The incidence of new-onset DM was 1.5% (34/2270) in the colchicine group and 2.2% (49/2245) in the placebo group (p=0.10). Participants with DM were at higher risk for all endpoints. The primary composite end point in the placebo group occurred in 13.0% (67/515) patients with DM and in 8.8% (197/2245) of the patients without DM (unadjusted hazard ratio 1.54 [95% CI 1.16–2.03, p<0.01]) compared to the group without DM. DM was also strongly associated with the occurrence of all secondary end points. Conclusion This study shows that the beneficial effects of colchicine on cardiovascular endpoints are consistent regardless of DM status. The data indicate that larger trials are needed to assess whether colchicine reduces the incidence of new-onset DM. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): National Health Medical Research Council of Australia and the Netherlands Organization for Health Research and Development

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)0.005
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.003
GPT teacher head0.193
Teacher spread0.191 · 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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