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Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators. | LitMetric

AI Article Synopsis

  • There is growing interest in the additional benefits of metformin, beyond its primary use for managing type 2 diabetes, including potential protective effects against cardiovascular issues, cognitive decline, and cancer.
  • This study compared cancer risk in patients using metformin versus those using sulfonylureas, using a large dataset from the UK, to investigate if metformin offers any significant protective effect against cancer over a 6-year period.
  • The results indicated that metformin did not significantly reduce cancer risk compared to sulfonylureas, and the analysis faced challenges including the disparity of data overlap and the issue of pre-cancer deaths affecting the outcomes.

Article Abstract

There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and metformin use has also been associated with reduced cognitive decline and cancer incidence. In this paper, we dig more deeply into whether metformin prevents cancer by emulating target randomized trials comparing metformin to sulfonylureas as first line diabetes therapy using data from Clinical Practice Research Datalink, a U.K. primary care database (1987-2018). We included individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated metformin (N=93353) or a sulfonylurea (N=13864). In our cohort, the estimated overlap weighted additive separable direct effect of metformin compared to sulfonylureas on cancer risk at 6 years was -1% (.95 CI=-2.2%, 0.1%), which is consistent with metformin providing no direct protection against cancer incidence or substantial protection. The analysis faced two methodological challenges-poor overlap, and pre-cancer death as a competing risk. To address these issues while minimizing nuisance model misspecification, we develop and apply double/debiased machine learning estimators of overlap weighted separable effects in addition to more traditional effect estimates.

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Source
http://dx.doi.org/10.1093/aje/kwae217DOI Listing

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