Evaluating Metformin Strategies for Cancer Prevention: A Target Trial Emulation Using Electronic Health Records

Peri-operative anti-inflammatory drug use and seizure recurrence after resective epilepsy surgery: Target trials emulation

We conducted a retrospective observational study to examine whether anti-inflammatory medications prescribed peri-operatively of resective brain surgery can reduce long-term seizure recurrence for individuals with drug-resistant focal epilepsy. We used insurance-claims data from across the United States to screen medications prescribed to 1,993 individuals undergoing epilepsy. We then validated the results in a well-characterized cohort of 671 epilepsy patients from a major surgical center. Twelve medications met the screening criteria and were evaluated, identifying dexamethasone and zonisamide as potentially beneficial. Dexamethasone reduced seizure recurrence by 42% over 9 years of follow-up (hazard-ratio = 0.742; 95% CI = 0.662, 0.831), and zonisamide reduced recurrence by 33% (HR = 0.782; 95% CI = 0.667, 0.917). While dexamethasone could not be validated, analysis of zonisamide in the clinical cohort corroborated the beneficial effect (HR = 0.828; 95% CI = 0.706, 0.971). If prospectively validated, this study suggests surgeons could improve long-term outcomes of epilepsy surgery by medically reducing neuro-inflammation in the surgical bed.

Insulin and Insulin-like Growth Factor and Risk of Postmenopausal Estrogen Receptor-Positive Breast Cancer: A Case-Cohort Analysis

BACKGROUND

Higher concentration of insulin-like growth factor-1 (IGF-1) increases postmenopausal breast cancer risk, but evidence for insulin and c-peptide is limited. Furthermore, not all studies have accounted for potential confounding by biomarkers from other biological pathways, and not all were restricted to estrogen receptor (ER)-positive breast cancer.

METHODS

This was a case-cohort study of 1,223 postmenopausal women (347 with ER-positive breast cancer) from the Melbourne Collaborative Cohort Study. We measured insulin, c-peptide, IGF-1, insulin-like growth factor binding protein-3, and biomarkers of inflammatory and sex-steroid hormone pathways. Poisson regression with a robust variance estimator was used to estimate risk ratios (RR) and 95% confidence intervals (95% CI) for ER-positive breast cancer per doubling plasma concentration and for quartiles, without and with adjustment for other, potentially confounding biomarkers.

RESULTS

ER-positive breast cancer risk was not associated with doubling of insulin (RR = 0.97, 95% CI, 0.82-1.14) or c-peptide (RR = 1.01, 95% CI, 0.80-1.26). Risk seemed to decrease with doubling IGF-1 (RR = 0.80, 95% CI, 0.62-1.03) and insulin-like growth factor binding protein-3 (RR = 0.62, 95% CI, 0.41-0.90). RRs were not meaningfully different when exposures were modeled as quartiles. RRs were less than unity but imprecise after adjustment for inflammatory and sex-steroid hormone biomarkers.

CONCLUSIONS

Circulating insulin, c-peptide, and IGF-1 were not positively associated with risk of ER-positive breast cancer in this case-cohort analysis of postmenopausal women.

IMPACT

Associations between insulin and c-peptide and risk of ER-positive breast cancer in postmenopausal women are likely to be weak.

Modern Evidence Generation on Medication Effectiveness and Safety During Pregnancy: Study Design Considerations

Non-randomized studies will remain the mainstay for evidence on medications' effects in pregnancy since the number of pregnant participants in randomized clinical trials is insufficient to evaluate uncommon but serious pregnancy outcomes. There has been a growing interest in conceptualizing causal inference based on observational data as an attempt to emulate a hypothetical randomized trial: the target trial. This approach can help identify design flaws and ensuing biases and can point toward potential solutions. Adoption of the target trial emulation framework in perinatal studies raises unique challenges due to the distinct role of gestational time. Challenges include, among others, identifying the timing of conception, pregnancy losses as competing events for later outcomes, different etiologically relevant time windows depending on the outcome, and time-varying outcome risks. We discuss various considerations in developing a protocol for a target trial evaluating drug effects in pregnancy and its observational emulation in databases and registries. While not a panacea, the framework offers a valuable tool to guide us through the specification of the causal questions, the study population and the treatment strategies to be compared and helps to identify avoidable biases as well as unavoidable deviations from the optimal protocol. Making these deviations explicit elucidates the assumptions we make when drawing causal conclusions, and the types of analyses that can be undertaken to quantify the potential magnitude of such biases. Such discipline in the design, conduct, and reporting of pregnancy studies will ultimately lead to the best information possible to inform treatment decisions during pregnancy.

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

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 the Clinical Practice Research Datalink, a UK primary-care database (1987-2018). We included 93 353 individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated use of metformin (n = 79 489) or a sulfonylurea (n = 13 864). In our cohort, the estimated overlap-weighted additive separable direct effect of metformin compared with sulfonylureas on cancer risk at 6 years was -1 percentage point (95% CI, -2.2 to 0.1), which is consistent with metformin's providing no direct protection against cancer incidence or substantial protection. The analysis faced 2 methodological challenges: (1) poor overlap and (2) precancer 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. This article is part of a Special Collection on Pharmacoepidemiology.

Clinical significance of lipid pathway-targeted therapy in breast cancer

Globally, breast cancer represents the most common cancer and the primary cause of death by cancer in women. Lipids are crucial in human physiology, serving as vital energy reserves, structural elements of biological membranes, and essential signaling molecules. The metabolic reprogramming of lipid pathways has emerged as a critical factor in breast cancer progression, drug resistance, and patient prognosis. In this study, we delve into the clinical implications of lipid pathway-targeted therapy in breast cancer. We highlight key enzymes and potential therapeutic targets involved in lipid metabolism reprogramming, and their associations with cancer progression and treatment outcomes. Furthermore, we detail the clinical trials exploring the anticancer and cancer chemopreventive activity of therapies targeting these molecules. However, the clinical efficacy of these therapies remains controversial, highlighting the urgent need for predictive biomarkers to identify patient subpopulations likely to benefit from such treatment. We propose the Selective Lipid Metabolism Therapy Benefit Hypothesis, emphasizing the importance of personalized medicine in optimizing lipid pathway-targeted therapy for breast cancer patients.

A Narrative Review: Repurposing Metformin as a Potential Therapeutic Agent for Oral Cancer

Oral cancer, particularly oral squamous cell carcinoma (OSCC), is a significant global health challenge because of its high incidence and limited treatment options. Major risk factors, including tobacco use, alcohol consumption, and specific microbiota, contribute to the disease's prevalence. Recently, a compelling association between diabetes mellitus (DM) and oral cancer has been identified, with metformin, a widely used antidiabetic drug, emerging as a potential therapeutic agent across various cancers, including OSCC. This review explores both preclinical and clinical studies to understand the mechanisms by which metformin may exert its anticancer effects, such as inhibiting cancer cell proliferation, inducing apoptosis, and enhancing the efficacy of existing treatments. Preclinical studies demonstrate that metformin modulates crucial metabolic pathways, reduces inflammation, and impacts cellular proliferation, thereby potentially lowering cancer risk and improving patient outcomes. Additionally, metformin's ability to reverse epithelial-to-mesenchymal transition (EMT), regulate the LIN28/let-7 axis, and its therapeutic role in head and neck squamous cell carcinoma (HNSCC) are examined through experimental models. In clinical contexts, metformin shows promise in enhancing therapeutic outcomes and reducing recurrence rates, although challenges such as drug interactions, complex dosing regimens, and risks such as vitamin B12 deficiency remain. Future research should focus on optimizing metformin's application, investigating its synergistic effects with other therapies, and conducting rigorous clinical trials to validate its efficacy in OSCC treatment. This dual exploration underscores metformin's potential to play a transformative role in both diabetes management and cancer care, potentially revolutionizing oral cancer treatment strategies.

Metformin and other anti-diabetic medication use and breast cancer incidence in the Nurses' Health Studies

We aimed to examine the association between the use of metformin and other anti-diabetic medications and breast cancer incidence within two large prospective cohort studies. We followed 185,181 women who participated in the Nurses' Health Study (NHS; 1994-2016) and the NHSII (1995-2017), with baseline corresponding to the date metformin was approved for type 2 diabetes (T2D) treatment in the US Information on T2D diagnosis, anti-diabetes medications, and other covariates was self-reported at baseline and repeatedly assessed by follow-up questionnaires every 2 years. Breast cancer cases were self-reported and confirmed by medical record review. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between medication use and breast cancer were estimated using Cox proportional hazards regression models, adjusting for breast cancer risk factors. During 3,324,881 person-years of follow-up, we ascertained 9,192 incident invasive breast cancer cases, of which 451 were among women with T2D. Compared with women without T2D (n = 169,263), neither metformin use (HR = 0.97; 95% CI = 0.81-1.15) nor other anti-diabetic medications use (HR = 1.11; 95% CI = 0.90-1.36) associated with significantly lower breast cancer incidence. Among women with T2D (n = 15,918), compared with metformin never users, metformin ever use was not significantly inversely associated with breast cancer (HR = 0.92; 95% CI = 0.74-1.15). Although we observed that past use of metformin was inversely associated with breast cancer in the T2D population (HR = 0.67; 95% CI = 0.48-0.94), current use (HR = 1.01; 95% CI = 0.80-1.27) and longer duration of metformin use were not associated with breast cancer (each 2-year interval: HR = 1.01; 95% CI = 0.95-1.07). Overall, metformin use was not associated with the risk of developing breast cancer among the overall cohort population or among women with T2D.

Does metformin really reduce prostate cancer risk: an up-to-date comprehensive genome-wide analysis

BACKGROUND

The relationship between metformin use and prostate cancer (PCa) risk has yet to be clear despite more than a decade of debate on this topic. Hence, we aimed to investigate the causal role of metformin in reducing PCa risk through an up-to-date comprehensive genome-wide analysis.

METHODS

We employed validated instrument variables of metformin use derived from a prior high-quality study, including five potential targets (AMPK, GCG, GDF15, MCI and MG3). Mendelian randomization (MR) analysis was performed to harmonize genetically predicted metformin use and PCa phenotypes. PCa phenotypes were from two large genome-wide association studies (GWAS), the Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome (PRACTICAL) and the FinnGen cohort. Seven methods were applied to generate MR results: the inverse variance weighted (IVW), IVW with multiplicative random effects, MR-Egger, MR-Egger (bootstrap), weighted median, simple mode and weighted mode. Strict sensitivity analysis was conducted to satisfy core assumptions of MR design.

RESULTS

We enrolled 32 significant single nucleotide polymorphisms (SNPs) that involved with metformin use. Nearly all targets yielded insignificant primary results (IVW with multiplicative random effects), except that AMPK target posed a positive effect on PCa risk from FinnGen cohort [odds ratio (OR): 6.09, 95% confidence interval (CI): 1.10-33.53, P value: 0.038]. The general effect of metformin use, comprising all 5 targets, also yielded negative results (random-effect meta-analysis with OR: 1.09, 95% CI: 0.76-1.58, P value: 0.637 for PRACTICAL; OR: 2.55, 95% CI: 0.58-11.16, P value: 0.215 for FinnGen). None of the sensitivity analyses provided support for a causal association between metformin use and PCa risk.

CONCLUSION

This up-to-date study did not support the protective role of metformin in reducing PCa risk, considering each target, overall effect, and sensitivity analysis. It is imperative to reflect on the presumed "almighty medicine" and ongoing phase III trials are anticipated to assess the anti-neoplasm effect of metformin.

Patients with oral tongue squamous cell carcinoma and co‑existing diabetes exhibit lower recurrence rates and improved survival: Implications for treatment

Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recurrence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2-year survival (P=0.025) compared with non-diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recurrence and improved survival compared with non-diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply that the therapeutic manipulation of glucose levels used to treat diabetes may be useful for patients with SCCOT regardless of their diabetic status. Further studies are warranted to investigate the impact of diabetes in other SCCHN subtypes.