Glucose-lowering medications and the risk of cancer: A methodological review of studies based on real-world data

Comparative estimation of the effects of antihypertensive medications on schizophrenia occurrence: a multinational observational cohort study

BACKGROUND

The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics.

METHODS

Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia.

RESULTS

5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43]).

CONCLUSIONS

The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years.

Predictors of frequent emergency department visits among hospitalized cancer patients: a comparative cohort study using integrated clinical and administrative data to improve care delivery

BACKGROUND

Frequent emergency department (FED) visits by cancer patients represent a significant burden to the health system. This study identified determinants of FED in recently hospitalized cancer patients, with a particular focus on opioid use.

METHODS

A prospective cohort discharged from surgical/medical units of the McGill University Health Centre was assembled. The outcome was FED use (≥ 4 ED visits) within one year of discharge. Data retrieved from the universal health insurance system was analyzed using Cox Proportional Hazards (PH) model, adopting the Lunn-McNeil approach for competing risk of death.

RESULTS

Of 1253 patients, 14.5% became FED users. FED use was associated with chemotherapy one-year pre-admission (adjusted hazard ratio (aHR) 2.60, 95% CI: 1.80-3.70), ≥1 ED visit in the previous year (aHR: 1.80, 95% CI 1.20-2.80), ≥15 pre-admission ambulatory visits (aHR 1.54, 95% CI 1.06-2.34), previous opioid and benzodiazepine use (aHR: 1.40, 95% CI: 1.10-1.90 and aHR: 1.70, 95% CI: 1.10-2.40), Charlson Comorbidity Index ≥ 3 (aHR: 2.0, 95% CI: 1.2-3.4), diabetes (aHR: 1.60, 95% CI: 1.10-2.20), heart disease (aHR: 1.50, 95% CI: 1.10-2.20) and lung cancer (aHR: 1.70, 95% CI: 1.10-2.40). Surgery (cardiac (aHR: 0.33, 95% CI: 0.16-0.66), gastrointestinal (aHR: 0.34, 95% CI: 0.14-0.82) and thoracic (aHR: 0.45, 95% CI: 0.30-0.67) led to a decreased risk of FED use.

CONCLUSIONS

Cancer patients with higher co-morbidity, frequent use of the healthcare system, and opioid use were at increased risk of FED use. High-risk patients should be flagged for preventive intervention.

The Application of Lag Times in Cancer Pharmacoepidemiology: A Narrative Review

With the increasing utilization of medications worldwide, coupled with the increasing availability of long-term data, there is a growing opportunity and need for robust studies evaluating drug-cancer associations. One methodology of importance in such studies is the application of lag times. In this review, we discuss the main reasons for using lag times. Namely, we discuss the typically long latency period of cancer concerning both tumor promoter and initiator effects and outline why cancer latency is a key consideration when choosing a lag time. We also discuss how the use of lag times can help reduce protopathic and detection bias. Finally, we present practical advice for implementing lag periods. In general, we recommend that researchers consider the information that generated the hypothesis as well as clinical and biological knowledge to inform lag period selection. In addition, given that latency periods are usually unknown, we also advocate that researchers examine multiple lag periods in sensitivity analyses as well as duration analyses and flexible modeling approaches.

Reproducibility of real-world evidence studies using clinical practice data to inform regulatory and coverage decisions

Studies that generate real-world evidence on the effects of medical products through analysis of digital data collected in clinical practice provide key insights for regulators, payers, and other healthcare decision-makers. Ensuring reproducibility of such findings is fundamental to effective evidence-based decision-making. We reproduce results for 150 studies published in peer-reviewed journals using the same healthcare databases as original investigators and evaluate the completeness of reporting for 250. Original and reproduction effect sizes were positively correlated (Pearson’s correlation = 0.85), a strong relationship with some room for improvement. The median and interquartile range for the relative magnitude of effect (e.g., hazard ratio_original/hazard ratio_reproduction) is 1.0 [0.9, 1.1], range [0.3, 2.1]. While the majority of results are closely reproduced, a subset are not. The latter can be explained by incomplete reporting and updated data. Greater methodological transparency aligned with new guidance may further improve reproducibility and validity assessment, thus facilitating evidence-based decision-making. Study registration number: EUPAS19636.

Analyses of real-world evidence from digital clinical practice data provide important insights for healthcare decision makers. Here, authors test reproducibility of 150 peer-reviewed studies, reporting strong reproducibility, which could be further improved through more complete reporting in future original studies

Pioglitazone, Bladder Cancer and the Presumption of Innocence

BACKGROUND

Thiazolidinediones are potent exogenous agonists of PPAR-γ, which augment the effects of insulin to its cellular targets and mainly at the level of adipose tissue. Pioglitazone, the main thiazolidinedione in clinical practice, has shown cardiovascular and renal benefits in patients with type 2 diabetes, durable reduction of glycated hemoglobulin levels, important improvements of several components of the metabolic syndrome and beneficial effects of non-alcoholic fatty liver disease.

OBJECTIVE

Despite all of its established advantages, the controversy for an increased risk of developing bladder cancer, combined with the advent of newer drug classes that achieved major cardiorenal effects have significantly limited its use spreading a persistent shadow of doubt for its future role.

METHODS

Pubmed, Google and Scope databases have been thoroughly searched and relevant studies were selected.

RESULTS

This paper explores thoroughly both in vitro and in vivo (animal models and humans) studies that investigated the possible association of pioglitazone with bladder cancer.

CONCLUSION

Currently the association of pioglitazone with bladder cancer cannot be based on solid evidence. This evidence cannot justify its low clinical administration, especially in the present era of individualised treatment strategies. Definite clarification of this issue is imperative and urgently anticipated from future high quality and rigorous pharmacoepidemiologic research, keeping in mind its unique mechanism of action and its significant pleiotropic effects.

Prevalence of Avoidable and Bias-Inflicting Methodological Pitfalls in Real-World Studies of Medication Safety and Effectiveness

Many real-word evidence (RWE) studies that utilize existing healthcare data to evaluate treatment effects incur substantial but avoidable bias from methodologically flawed study design; however, the extent of preventable methodological pitfalls in current RWE is unknown. To characterize the prevalence of avoidable methodological pitfalls with potential for bias in published claims-based studies of medication safety or effectiveness, we conducted an English-language search of PubMed for articles published from January 1, 2010 to May 20, 2019 and randomly selected 75 studies (10 case-control and 65 cohort studies) that evaluated safety or effectiveness of cardiovascular, diabetes, or osteoporosis medications using US health insurance claims. General and methodological study characteristics were extracted independently by two reviewers, and potential for bias was assessed across nine bias domains. Nearly all studies (95%) had at least one avoidable methodological issue known to incur bias, and 81% had potentially at least one of the four issues considered major due to their potential to undermine study validity: time-related bias (57%), potential for depletion of outcome-susceptible individuals (44%), inappropriate adjustment for postbaseline variables (41%), or potential for reverse causation (39%). The median number of major issues per study was 2 (interquartile range (IQR), 1-3) and was lower in cohort studies with a new-user, active-comparator design (median 1, IQR 0-1) than in cohort studies of prevalent users with a nonuser comparator (median 3, IQR 3-4). Recognizing and avoiding known methodological study design pitfalls could substantially improve the utility of RWE and confidence in its validity.

Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments

Real-world evidence (RWE), the understanding of treatment effectiveness in clinical practice generated from longitudinal patient-level data from the routine operation of the healthcare system, is thought to complement evidence on the efficacy of medications from randomized controlled trials (RCTs). RWE studies follow a structured approach. (1) A design layer decides on the study design, which is driven by the study question and refined by a medically informed target population, patient-informed outcomes, and biologically informed effect windows. Imagining the randomized trial we would ideally perform before designing an RWE study in its likeness reduces bias; the new-user active comparator cohort design has proven useful in many RWE studies of diabetes treatments. (2) A measurement layer transforms the longitudinal patient-level data stream into variables that identify the study population, the pre-exposure patient characteristics, the treatment, and the treatment-emergent outcomes. Working with secondary data increases the measurement complexity compared to primary data collection that we find in most RCTs. (3) An analysis layer focuses on the causal treatment effect estimation. Propensity score analyses have gained in popularity to minimize confounding in healthcare database analyses. Well-understood investigator errors, like immortal time bias, adjustment for causal intermediates, or reverse causation, should be avoided. To increase reproducibility of RWE findings, studies require full implementation transparency. This article integrates state-of-the-art knowledge on how to conduct and review RWE studies on diabetes treatments to maximize study validity and ultimately increased confidence in RWE-based decision making.

Glycaemic index, glycaemic load and cancer risk: results from the prospective NutriNet-Santé cohort

BACKGROUND

Evidence is accumulating that high dietary glycaemic index (GI) and glycaemic load (GL) are potential risk factors for several metabolic disorders (e.g. type-2 diabetes, cardiovascular diseases), but remains limited concerning cancer risk. Although, mechanistic data suggest that consuming high-GI foods may contribute to carcinogenesis through elevated blood glucose levels, insulin resistance or obesity-related mechanisms. Our objective was to study the associations between dietary GI/GL and cancer.

METHODS

In total, 103 020 French adults (median age = 40.2 years) from the NutriNet-Santé cohort (2009-2020) with no cancer or diabetes at baseline were included (705 137 person-years, median follow-up time = 7.7 years). Repeated 24-h dietary records linked with a detailed food-composition table (>3500 food/beverage items). We computed the average dietary GI and GL at the individual level. Associations between GI, GL, contribution of low- and medium/high-GI foods to energy and carbohydrate intake and cancer risk (overall, breast, prostate and colorectal) were assessed using multivariable Cox proportional-hazard models.

RESULTS

Higher dietary GL was associated with higher overall cancer risk [n = 3131 cases, hazard ratios (HRs) for sex-specific quintile 5 vs 1 = 1.25, 95% confidence interval (CI) = 1.03-1.52; Ptrend = 0.008] and specifically postmenopausal breast cancer (n = 924, HRQ5vs.Q1 = 1.64, 95% CI = 1.06-2.55; Ptrend = 0.03). A higher contribution of low-GI food/beverages to energy intake was associated with lower cancer risk whereas a higher contribution of medium/high-GI items to energy intake was positively associated with higher risk of overall, breast and postmenopausal breast cancers (Ptrend ≤ 0.02).

CONCLUSIONS

These results support a possible impact of GI/GL on cancer risk. If confirmed in other populations and settings, dietary GI/GL could be considered as modifiable risk factors for primary cancer prevention.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT03335644.

Role of metformin and other metabolic drugs in the prevention and therapy of endocrine-related cancers

Metabolic syndrome is associated with chronic diseases, including type 2 diabetes, cardiovascular diseases, and cancer. This review summarizes the current evidence on the antitumor effects of some relevant drugs currently used to manage metabolic-related pathologies (i.e. insulin and its analogs, metformin, statins, etc.) in endocrine-related cancers including breast cancer, prostate cancer, pituitary cancer, ovarian cancer, and neuroendocrine neoplasms. Although current evidence does not provide a clear antitumor role of several of these drugs, metformin seems to be a promising chemopreventive and adjuvant agent in cancer management, modulating tumor cell metabolism and microenvironment, through both AMP-activated protein kinase-dependent and -independent mechanisms. Moreover, its combination with statins might represent a promising therapeutic strategy to tackle the progression of endocrine-related tumors. However, further studies are needed to endorse the clinical relevance of these drugs as adjuvants for cancer chemotherapy.

How well can we assess the validity of non-randomised studies of medications? A systematic review of assessment tools

OBJECTIVE

To determine whether assessment tools for non-randomised studies (NRS) address critical elements that influence the validity of NRS findings for comparative safety and effectiveness of medications.

DESIGN

Systematic review and Delphi survey.

DATA SOURCES

We searched PubMed, Embase, Google, bibliographies of reviews and websites of influential organisations from inception to November 2019. In parallel, we conducted a Delphi survey among the International Society for Pharmacoepidemiology Comparative Effectiveness Research Special Interest Group to identify key methodological challenges for NRS of medications. We created a framework consisting of the reported methodological challenges to evaluate the selected NRS tools.

STUDY SELECTION

Checklists or scales assessing NRS.

DATA EXTRACTION

Two reviewers extracted general information and content data related to the prespecified framework.

RESULTS

Of 44 tools reviewed, 48% (n=21) assess multiple NRS designs, while other tools specifically addressed case-control (n=12, 27%) or cohort studies (n=11, 25%) only. Response rate to the Delphi survey was 73% (35 out of 48 content experts), and a consensus was reached in only two rounds. Most tools evaluated methods for selecting study participants (n=43, 98%), although only one addressed selection bias due to depletion of susceptibles (2%). Many tools addressed the measurement of exposure and outcome (n=40, 91%), and measurement and control for confounders (n=40, 91%). Most tools have at least one item/question on design-specific sources of bias (n=40, 91%), but only a few investigate reverse causation (n=8, 18%), detection bias (n=4, 9%), time-related bias (n=3, 7%), lack of new-user design (n=2, 5%) or active comparator design (n=0). Few tools address the appropriateness of statistical analyses (n=15, 34%), methods for assessing internal (n=15, 34%) or external validity (n=11, 25%) and statistical uncertainty in the findings (n=21, 48%). None of the reviewed tools investigated all the methodological domains and subdomains.

CONCLUSIONS

The acknowledgement of major design-specific sources of bias (eg, lack of new-user design, lack of active comparator design, time-related bias, depletion of susceptibles, reverse causation) and statistical assessment of internal and external validity is currently not sufficiently addressed in most of the existing tools. These critical elements should be integrated to systematically investigate the validity of NRS on comparative safety and effectiveness of medications. SYSTEMATIC REVIEW PROTOCOL AND REGISTRATION: https://osf.io/es65q.

DPP4i, thiazolidinediones, or insulin and risks of cancer in patients with type 2 diabetes mellitus on metformin-sulfonylurea dual therapy with inadequate control

INTRODUCTION

This study aims to compare the risks of cancer among patients with type 2 diabetes mellitus (T2DM) on metformin-sulfonylurea dual therapy intensified with dipeptidyl peptidase 4 inhibitors (DPP4i), thiazolidinediones, or insulin.

RESEARCH DESIGN AND METHODS

We assembled a retrospective cohort data of 20 577 patients who were free of cancer and on metformin-sulfonylurea dual therapy, and whose drug treatments were intensified with DPP4i (n=9957), insulin (n=7760), or thiazolidinediones (n=2860) from January 2006 to December 2017. Propensity-score weighting was used to balance out baseline covariates across the three groups. HRs for any types of cancer, cancer mortality, and all-cause mortality were assessed using Cox proportional-hazards models.

RESULTS

Over a mean follow-up period of 34 months with 58 539 person-years, cumulative incidences of cancer, cancer mortality, and all-cause mortality were 0.028, 0.009, and 0.072, respectively. Patients intensified with insulin had the highest incidence of all-cause mortality (incidence rate=3.22/100 person-years) and the insulin itself posed the greatest risk (HR 2.46, 95% CI 2.25 to 2.70, p<0.001; 2.44, 95% CI 2.23 to 2.67) compared with thiazolidinediones and DPP4i, respectively. Comparing between thiazolidinediones and DPP4i, thiazolidinediones was associated with higher risk of cancer (HR 1.43, 95% CI 1.25 to 1.63) but not cancer mortality (HR 1.21, 95% CI 0.92 to 1.58) and all-cause mortality (HR 0.99, 95% CI 0.88 to 1.11). Insulin was associated with the greatest risk of cancer mortality (HR 1.36, 95% CI 1.09 to 1.71; 1.65, 95% CI 1.31 to 2.07) compared with thiazolidinediones and DPP4i, respectively.

CONCLUSIONS

For patients with T2DM on metformin-sulfonylurea dual therapy, the addition of DPP4i was the third-line medication least likely to be associated with cancer mortality and cancer effect among three options, and posed no increased risk for all-cause mortality when compared with thiazolidinediones.

Using RWE research to extend clinical trials in diabetes: An example with implications for the future

BACKGROUND

Although randomized, controlled trials (RCTs) are seen as the gold standard for evidence in clinical medicine, a number of considerations are increasing the use of real-world data (RWD) to generate evidence. A series of methodological challenges must be overcome in order for such real-world evidence (RWE) to gain acceptance. In diabetes, RWE faces some particular issues that have limited its development. As the natural history of diabetes progresses, patients' disease changes over time and treatments will be modified as a result. This evolving disease and treatment pattern requires application of methods that account for such changes over time. Research developing RWE in diabetes and other conditions has sometimes been subject to important biases, and researchers should be aware of, and take steps to mitigate potential for bias in order to enhance the evidence produced.

RESULTS

We review a RWE study that replicated and extended evidence provided by a RCT regarding the effects of weekly exenatide relative to basal insulin (glargine or detemir) to illustrate a potential application of RWE. This study observed a 0.7% decrease in HbA1C for weekly exenatide relative to a 0.5% decrease in HbA1C for the comparator along with a 2 kg weight loss for weekly exenatide relative to a 0.25 kg weight gain, effects that were close to those from the RCT. Further, the RWE study was able to extend results to patient populations that were not well represented in the RCT.

CONCLUSION

Despite numerous challenges, RWE can be used to complement evidence from RCTs.

Transparency in real-world evidence (RWE) studies to build confidence for decision-making: Reporting RWE research in diabetes

Transparency of real-world evidence (RWE) studies is critical to understanding how findings of a specific study were derived and is a necessary foundation to assessing validity and determination of whether decisions should be informed by the findings. In the present paper, we lay out strategies to improve clarity in the reporting of comparative effectiveness studies using real-world data that were generated by the routine operation of a healthcare system. This may include claims data, electronic health records, wearable devices, patient-reported outcomes or patient registries. These recommendations were discussed with multiple stakeholders, including regulators, payers, academics and journal editors, and endorsed by two professional societies that focus on RWE. We remind readers interested in diabetes research of the utility of conceptualizing a target trial that is then emulated by a RWE study when planning and communicating about RWE study implementation. We recommend the use of a graphical representation showcasing temporality of key longitudinal study design choices. We highlight study elements that should be reported to provide the clarity necessary to make a study reproducible. Finally, we suggest registering study protocols to increase process transparency. With these tools the readership of diabetes RWE studies will be able to more efficiently understand each study and be more able to assess a study's validity with reasonably high confidence before making decisions based on its findings.

The present and future scope of real-world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future?

The last decade has witnessed an exponential growth in the opportunities to collect and link health-related data from multiple resources, including primary care, administrative, and device data. The availability of these "real-world," "big data" has fuelled also an intense methodological research into methods to handle them and extract actionable information. In medicine, the evidence generated from "real-world data" (RWD), which are not purposely collected to answer biomedical questions, is commonly termed "real-world evidence" (RWE). In this review, we focus on RWD and RWE in the area of diabetes research, highlighting their contributions in the last decade; and give some suggestions for future RWE diabetes research, by applying well-established and less-known tools to direct RWE diabetes research towards better personalized approaches to diabetes care. We underline the essential aspects to consider when using RWD and the key features limiting the translational potential of RWD in generating high-quality and applicable RWE. Only if viewed in the context of other study designs and statistical methods, with its pros and cons carefully considered, RWE will exploit its full potential as a complementary or even, in some cases, substitutive source of evidence compared to the expensive evidence obtained from randomized controlled trials.

More recent, better designed studies have weakened links between antidiabetes medications and cancer risk

BACKGROUND

An increasing number of studies have investigated associations of antidiabetes medications with cancer risk. Antidiabetes medications are classified by their mechanisms of action on tissues and organs. They potentially act as both causative and confounding factors in the temporal association of diabetes and cancer.

AIM

To present the current evidence regarding both the carcinogenic and anti-carcinogenic effects of antidiabetes medications on cancer in humans.

METHODS

A review of the scientific literature.

RESULTS

The most conclusive evidence shown of an association of antidiabetes medication with a specific cancer was for that of the thiazolidinedione pioglitazone with bladder cancer. Currently, there is inconclusive evidence regarding a possible association of incretin therapies, drugs of the dipeptidyl peptidase-4 inhibitor class, with the risk of pancreatic cancer. Insulin, sulfonylureas, metformin and sodium-glucose co-transporter-2 inhibitors appear not to be associated with increased risk of any cancer. Sparse evidence suggests possible protective effects against cancer incidence of metformin, sulfonylureas, thiazolidinediones, incretin-based drugs and sodium-glucose co-transporter-2 inhibitors.

CONCLUSION

The conflicting evidence regarding associations of antidiabetes medications with cancer risk is apparently attributable to both methodological issues and to the complexity of the subject. More recent and better-designed studies have weakened the evidence for links between antidiabetes medications and cancer risk.