Glucagon-like Peptide-1 Receptor Agonists And Pancreatic Cancer: A Meta-analysis With Trial Sequential Analysis

Regardless of language, all eligible trials were considered for review. The inclusion criteria were as follows: (1) RCTs, (2) GLP-1 agonist use versus any comparator, (3) treatment for at least 48 weeks, (4) definition of events of pancreatic cancer, (5) inclusion of patients ≥ 18 y old, and (6) diagnosis of type 2 diabetes according to the American Diabetes Association criteria15. For trials that fulfilled all inclusion criteria but did not mention pancreatic cancer events, an e-mail was sent to the corresponding author asking for the data. Of 17 e-mails sent, four e-mails were returned to sender (the e-mail of the author did not exist or had changed) and 4 e-mails received replies, two of them containing pancreatic cancer data. Two independent investigators (L.C.P. Studies that met the inclusion criteria, or those with abstracts that lacked information to decide upon their exclusion, were included in full-text evaluation. Both investigators also analysed full texts and extracted data.



In order to assess the quality of studies, the Cochrane Collaboration tool for risk of bias was used16. Regarding risk of bias, MedicGLP Website we considered the non-adjudication of events to be "other bias". Quality of meta-analysis was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach17. We compared the events of interest in patients randomized to use of GLP-1 agonists versus the events in patients randomized to the control strategy (placebo or other antihyperglycemic medications). The outcome of interest was pancreatic cancer. Data were summarized with Mantel-Haenszel odds ratio (OR) with direct meta-analysis to compare the GLP-1 agonist group with the control group. Heterogeneity was assessed by the Cochran Q test (p-value of 0.1 was considered statistically significant) and the I2 test (values greater than 50% were considered to indicate elevated statistical heterogeneity). We performed a TSA on the identified studies to address whether the current evidence might be sufficient for firm conclusions. This analysis is associated with a cumulative meta-analysis represented by the Z-curve.



Therefore, we were able to estimate the sample size required to accept or reject a minimal difference between GLP-1 agonists and control18,19. This difference is arbitrary and must be clinically relevant. We set it as an absolute difference of 0.1% between groups, which is more conservative than the difference found in previous trials20. We conducted the TSA with an overall 5% risk of type I error and 20% risk of type II error (power of 80%). In this way, the analysis is able to reach a number needed to harm (NNH) of at least 1000. For studies with zero events in both arms, continuity correction was performed, and their data were included in TSA analyses. Publication bias was evaluated with a visual inspection of funnel plots and with Begg’s and Egger’s tests. If a small study bias was identified, we then performed the trim and fill computation to explore the effect of missing studies on the outcomes. The analyses were performed using RevMan software version 5.3 (Cochrane Collaboration, Copenhagen, Denmark) and STATA 12.0 (Stata Inc., College Station, Texas, USA). The TSA was performed with TSA software (Centre for Clinical Intervention Research Department, Copenhagen, Denmark).



Science continues to prove that the old adage "you are what you eat" is profoundly true. This is because you have trillions of microorganisms living in your gut, which are directly responsible for nearly every aspect of your health. These microbes make up what’s called your "gut microbiota." They are especially influential in determining your likelihood of gaining excessive weight, becoming obese, and developing obesity-related diseases like diabetes, heart disease, and premature death. The gut microbiome (the expressed genes of your gut microbiota) plays a major role in your metabolism through energy production, storage, and expenditure. So, it’s no surprise that science has found a strong connection between gut microbiome imbalance and metabolic syndrome. Yikes! If you have the wrong mix of microbes, MedicGLP) it could be making it tough for you to get your health on track! Did you know: Scientists can actually look at your gut microbiome composition and tell with 90% accuracy if you are obese or lean. That’s pretty impressive, isn’t it?



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