This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.
Prostate cancer (PCa) is the most common malignancies and one of the leading causes of death in elderly men.More than 50% male patientsdiagnosed with PCaare older than 65 years, andnearly 25% patients diagnosed with PCa are older than 75 years. It is surprising to find that metformin as themostwidely usedoral hypoglycemicdrug forType 2 diabetes could also inhibit the growth of prostate cancer cells in vitro [2,3]. However, clinical studies investigating the association between the use of metformin and the risk of PCa occurrence haveshown controversial results.Although there have been comprehensivemeta-analysis showing that the use of metformin had no effect on the risk of PCa occurrence ,butits effect on biochemical relapse (BCR) and mortality in prostate cancer(Pca) patients with type 2 diabetes are also controversial.So,the objective of this study was to comprehensively summarize the results to evaluate its effect on biochemical relapse and mortality for Pca patients with type 2diabete.
2 Materials and Methods
2.1 Literature Search Strategy and Study Selection
A comprehensive literature search was performed in the Medline , Web of Science and Cochrane Library databases from their earliest available date to Oct 31,2014. The following main key words were used: “metformin or biguanides, antidiabetic medication, hypoglycemic/ glucose-lowering drugs” combined with “prostate neoplasms” or “prostate cancer”.The search was extended to observational studies due to the absence of sufficient data from randomized clinical trials(RCTs). Criteria for inclusion of an article in the analysis were as follows:ï¼ˆ1ï¼‰cohort ,case-controlled studies or RCTs that assessed the efficacy of metformin on PCa;ï¼ˆ2ï¼‰studies that had sufficient information to allow adequate estimation of the hazard ratio (HR) and 95% confidence intervals (95CIS%) (or data to calculate these). The following exclusion criteria were used:(1) the inclusion criteria not being met; (2) two or more studies were reported by the same institution, the most recent or complete one was included to avoid overlapping populations.
2.2 Quality Assessment and Data Extraction
The methodological quality of randomized controlled trials (RCTs) was appraised in reference to the Jadad composite scale[5,6] and non-RCTs was assessed with the Newcastle–Ottawa Scale . The following data were extracted from each included study: the first author’s name, publication year, study design, comparison of groups , length of follow-up, adjusting variables, adjusted or crude HR and their 95%CIS. We listed the original data in the table, and default non-metformin use patients as reference group. For data that provided metformin-use patients as reference group, we converted it according to the methods mentioned in Jayne F Tierney’s literature. Three independent reviewers (Dong WP , Liu Q, LI T) completed this procedure and all disagreements were resolved by consensus.
2.3 Statistical Analysis
The pooling method was adopted,as the inverse-variance weighted mean of the logarithm of HR (defined as SHR) with its 95% CIS,to assess the strength of associations between metformin intake and BCR and mortality. We pooled the original estimates by using the random-effects model. The I2of Higgins and Thompson was used to assess heterogeneity among studies. In the presence of substantial heterogeneity (I2>50%), the random effects model (REM) was adopted as the pooling method, or otherwise using the fixed-effects model. Publication bias was evaluated using Begg’s. All statistical analyses were performed with STATA Software Program version 12.0.The article was written under the guidence of MOOSE statement due to the absence of randomized clinical trials.
Figure 1 shows the flow diagram for the study inclusion. We identified 744 related articles on the basis of the titles and abstracts, of which 706 were excluded because they were not related to the study objective or duplicate articles, and the remaining38 articles were of interest and their full texts were retrieved for further evaluation. Of the remaining 38 studies, 22were not included because articles only revelent PCa incidence and 5 were further excluded because they did not satisfy the inclusion criteria. Finally, 11[10-20] observational studies were included for this Meta-analysis. All but three[14,16,17] articles have given the follow-up time,four studies[10,11,12,14] concentrated on the role of metformin use on risk of biochemical recurrence and mortality following radical prostatectomy. Adjusting variables varied significantly between studies should be noticed, although all but three[10,16,18] articles have illustrated.The summary risk estimates for metformin and BCR are plotted in Figure 2. As shown by the random effects model(Figure3), the SHR of all-cause and PCa-specific mortality risk for metformin users vs. comparable groups was 0.80 (95%CIS:0.76-0.84) and 0.70(0.43-0.97) separately. There was no significant publication bias for studies investigating the use of metformin and BCR(P=0.22) and mortality(P=0.74), as assessed by Begger funnel plot(Figure4).
According to the overall SHR, we believe that metformin can reduce all-cause and PCa-specific mortality, but the results from different studies varied greatly. The noticeable I2 values indicated that the range of the incidence risk estimates was wide and these findings may reflect the different treatment methods in comparator groups and/or different epidemiological characteristics among the various populations included in our study. The significant HR for BCR risk reduction after ERBT may illustrate that the effect of metformin on BCR in PCa patients was affected by treatment methods and thus more clinic trials are expected. Positive surgical margins, seminal vesicle invasion and positive lymph nodes are risk factors for BCR, and differences in clinicopathologic features might have driven the results of BCR in PCa patients. However, no in vivo and in vitro studies explained why metformin had impact on BCR in PCa. Previous meta-analysis confirmed that Type 2 diabetes was inversely associated with the risk of developing PCa, and the reason was reportedly to be associated with the testosterone level in male diabetic patients. In vivo and vitro studies have shown that Type 2 diabetes patients have significant hyperinsulinemia and hyperglycemia. Insulin and insulin analogs can activate insulin receptor and IGF-1 receptor,which is mitogenic to promote the development and progression of cancer. There is evidence that increased glucose uptake in cancer cells can promote cells to produce oxidative stress and promote DNA damage, thus increasing the probability of carcinogenesis . Metformin exerts its antitumor effect in an insulin-dependent and tumor direct insulin-independent manner, and the main effect is the activation of adenosine monophosphate activated protein kinase (AMPK) via LKB-1, a tumor suppressor protein kinase. During cellular oxidative stress, AMPK suppresses protein synthesis ,gluconeogenesis and suppress cell growth by inactivation of mammalian target of rapamycin (mTOR), which is often activated in malignant cells . Metformin can also induce cell cycle arrest and apoptosis by inhibiting hepatic gluconeogenesis and the mTOR pathway and reducing growth factor signaling.The crucial effects of potential confounding factors on both the incidence of cancer in diabetic patients and metformin for the treatment of diabetes vs.other interventions cannot be ignored. Obesity, smoking, lack of exercise, smoking, drinking and diet are the most common lifestyle-related risk factors. Others include prescription age, race, non-steroidal anti-inflammatory drugs or other hypoglycemic agents, and other coexisting diseases. Adjustment variables are associated with the onset of PCa. Patients with PSA, gleason grade, surgical margins, seminal vesicle invasion, together with the factors mentioned above constitute prognostic variables. Diabetes, PCa, metformin and the confounding factors interact with each other, any change of which may affect the incidence and prognostic characteristics. As all were retrospective studies, our results inevitably have some limitations. First, we failed to fully adjust the confounding factors and biases in this meta-analysis, which may have caused the results less valid. Secondly, it is also crucial to beware that the study populations and comparators were heterogeneous, most probably because of the diversity of the study designs and ethnicities, and that the sensitivity of PCa to metformin may vary. Finally, the lack of a standardized treatment protocol in some studies(without providing the time and dose of drug administration) might explain the probability that other diabetes treatments may increase or reduce the risk of BCR and/or mortality may cause overestimation or underestimation of the effect of metformin. To the best of our knowledge, no other meta-analyses have been performed on the use of metformin and BCR and mortality of PCa. In summary, both PCa and Type 2 diabetes are age-related global diseases. Metformin as an inexpensive traditional oral hypoglycemic agent has been widely used for the treatment of Type 2 diabetes and our meta-analysis of observational studies manifests that metformin exposure may be associated with a reduction in BCR and mortality among PCa cancer patients with diabetes. It is necessary to conduct large multi-center randomized clinical trials to confirm the effect of metformin on PCa as methodologic limitations of individual studies may have introduced biases in these findings taken into account.