Systematic assessment of pharmaceutical prescriptions in association with cancer risk: a method to conduct a population-wide medication-wide longitudinal study

Integrated Pharmacogenetic Signature for the Prediction of Prostatic Neoplasms in Men With Metabolic Disorders

BACKGROUND/AIM

Oncogenic processes are delineated by metabolic dysregulation. Drug likeness is pharmacokinetically tested through the CYP450 enzymatic system, whose genetic aberrations under epigenetic stress could shift male organisms into prostate cancer pathways. Our objective was to predict the susceptibility to prostate neoplasia, focused on benign prostatic hyperplasia (BPH) and prostate cancer (PCa), based on the pharmacoepigenetic and the metabolic profile of Caucasians.

MATERIALS AND METHODS

Two independent cohorts of 47,389 individuals in total were assessed to find risk associations of CYP450 genes with prostatic neoplasia. The metabolic profile of the first cohort was statistically evaluated and frequencies of absorption-distribution-metabolism-excretion-toxicity (ADMET) properties were calculated. Prediction of miRNA pharmacoepigenetic targeting was performed.

RESULTS

We found that prostate cancer and benign prostatic hyperplasia patients of the first cohort shared common cardiometabolic trends. Drug classes C08CA, C09AA, C09CA, C10AA, C10AX of the cardiovascular, and G04CA, G04CB of the genitourinary systems, were associated with increased prostate cancer risk, while C03CA and N06AB of the cardiovascular and nervous systems were associated with low-risk for PCa. CYP3A4*1B was the most related pharmacogenetic polymorphism associated with prostate cancer susceptibility. miRNA-200c-3p and miRNA-27b-3p seem to be associated with CYP3A4 targeting and prostate cancer predisposition. Metabolomic analysis indicated that 11β-OHT, 2β-OHT, 15β-OHT, 2α-OHT and 6β-OHT had a high risk, and 16α-OHT, and 16β-OHT had an intermediate disease-risk.

CONCLUSION

These findings constitute a novel integrated signature for prostate cancer susceptibility. Further studies are required to assess their predictive value more fully.

Prescribed Drug Use and Aneurysmal Subarachnoid Hemorrhage Incidence: A Drug-Wide Association Study

BACKGROUND AND OBJECTIVES

Current benefits of invasive intracranial aneurysm treatment to prevent aneurysmal subarachnoid hemorrhage (aSAH) rarely outweigh treatment risks. Most intracranial aneurysms thus remain untreated. Commonly prescribed drugs reducing aSAH incidence may provide leads for drug repurposing. We performed a drug-wide association study (DWAS) to systematically investigate the association between commonly prescribed drugs and aSAH incidence.

METHODS

We defined all aSAH cases between 2000 and 2020 using International Classification of Diseases codes from the Secure Anonymised Information Linkage databank. Each case was matched with 9 controls based on age, sex, and year of database entry. We investigated commonly prescribed drugs (>2% in study population) and defined 3 exposure windows relative to the most recent prescription before index date (i.e., occurrence of aSAH): current (within 3 months), recent (3-12 months), and past (>12 months). A logistic regression model was fitted to compare drug use across these exposure windows vs never use, controlling for age, sex, known aSAH risk factors, and health care utilization. The family-wise error rate was kept at p < 0.05 through Bonferroni correction.

RESULTS

We investigated exposure to 205 commonly prescribed drugs between 4,879 aSAH cases (mean age 61.4, 61.2% women) and 43,911 matched controls. We found similar trends for lisinopril and amlodipine, with a decreased aSAH risk for current use (lisinopril odds ratio [OR] 0.63, 95% CI 0.44-0.90, amlodipine OR 0.82, 95% CI 0.65-1.04) and an increased aSAH risk for recent use (lisinopril OR 1.30, 95% CI 0.61-2.78, amlodipine OR 1.61, 95% CI 1.04-2.48). A decreased aSAH risk in current use was also found for simvastatin (OR 0.78, 95% CI 0.64-0.96), metformin (OR 0.58, 95% CI 0.43-0.78), and tamsulosin (OR 0.55, 95% CI 0.32-0.93). By contrast, an increased aSAH risk was found for current use of warfarin (OR 1.35, 95% CI 1.02-1.79), venlafaxine (OR 1.67, 95% CI 1.01-2.75), prochlorperazine (OR 2.15, 95% CI 1.45-3.18), and co-codamol (OR 1.31, 95% CI 1.10-1.56).

DISCUSSION

We identified several drugs associated with aSAH, of which 5 drugs (lisinopril and possibly amlodipine, simvastatin, metformin, and tamsulosin) showed a decreased aSAH risk. Future research should build on these signals to further assess the effectiveness of these drugs in reducing aSAH incidence.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that some commonly prescribed drugs are associated with subsequent development of aSAH.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Published registry-based pharmacoepidemiologic associations show limited concordance with agnostic medication-wide analyses

OBJECTIVES

To assess how the results of published national registry-based pharmacoepidemiology studies (where select associations are of interest) compare with an agnostic medication-wide approach (where all possible drug associations are tested).

STUDY DESIGN AND SETTING

We systematically searched for publications that reported drug associations with any, breast, colon/colorectal, or prostate cancer in the Swedish Prescribed Drug Registry. Results were compared against a previously performed agnostic medication-wide study on the same registry.

PROTOCOL

https://osf.io/kqj8n.

RESULTS

Most published studies (25/32) investigated previously reported associations. 421/913 (46%) associations had statistically significant results. 134 of the 162 unique drug-cancer associations could be paired with 70 associations in the agnostic study (corresponding drug categories and cancer types). Published studies reported smaller effect sizes and absolute effect sizes than the agnostic study, and generally used more adjustments. Agnostic analyses were less likely to report statistically significant protective associations (based on a multiplicity-corrected threshold) than their paired associations in published studies (McNemar odds ratio 0.13, P = 0.0022). Among 162 published associations, 36 (22%) showed increased risk signal and 25 (15%) protective signal at P < 0.05, while for agnostic associations, 237 (11%) showed increased risk signal and 108 (5%) protective signal at a multiplicity-corrected threshold. Associations belonging to drug categories targeted by individual published studies vs. nontargeted had smaller average effect sizes; smaller P values; and more frequent risk signals.

CONCLUSION

Published pharmacoepidemiology studies using a national registry addressed mostly previously proposed associations, were mostly "negative", and showed only modest concordance with their respective agnostic analyses in the same registry.

The association between prescription drugs and colorectal cancer prognosis: a nationwide cohort study using a medication-wide association study

BACKGROUND

With the availability of health insurance claim data, pharmacovigilance for various drugs has been suggested; however, it is necessary to establish an appropriate analysis method. To detect unintended drug effects and to generate new hypotheses, we conducted a hypothesis-free study to systematically examine the relationship between all prescription nonanticancer drugs and the mortality of colorectal cancer patients.

METHODS

We used the Korean National Health Insurance Service-National Sample Cohort database. A total of 2,618 colorectal cancer patients diagnosed between 2004 and 2015 were divided into drug discovery and drug validation sets (1:1) through random sampling. Drugs were classified using the Anatomical Therapeutic Chemical (ATC) classification system: 76 drugs classified as ATC level 2 and 332 drugs classified as ATC level 4 were included in the analysis. We used a Cox proportional hazard model adjusted for sex, age, colorectal cancer treatment, and comorbidities. The relationship between all prescription nonanticancer drugs and the mortality of colorectal cancer patients was analyzed, controlling for multiple comparisons with the false discovery rate.

RESULTS

We found that one ATC level-2 drug (drugs that act on the nervous system, including parasympathomimetics, addictive disorder drugs, and antivertigo drugs) showed a protective effect related to colorectal cancer prognosis. At the ATC level 4 classification, 4 drugs were significant: two had a protective effect (anticholinesterases and opioid anesthetics), and the other two had a detrimental effect (magnesium compounds and Pregnen [4] derivatives).

CONCLUSIONS

In this hypothesis-free study, we identified four drugs linked to colorectal cancer prognosis. The MWAS method can be useful in real-world data analysis.

Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review

PURPOSE

Signal detection is a crucial step in the discovery of post-marketing adverse drug reactions. There is a growing interest in using routinely collected data to complement established spontaneous report analyses. This work aims to systematically review the methods for drug safety signal detection using routinely collected healthcare data and their performance, both in general and for specific types of drugs and outcomes.

METHODS

We conducted a systematic review following the PRISMA guidelines, and registered a protocol in PROSPERO. MEDLINE, EMBASE, PubMed, Web of Science, Scopus, and the Cochrane Library were searched until July 13, 2021.

RESULTS

The review included 101 articles, among which there were 39 methodological works, 25 performance assessment papers, and 24 observational studies. Methods included adaptations from those used with spontaneous reports, traditional epidemiological designs, methods specific to signal detection with real-world data. More recently, implementations of machine learning have been studied in the literature. Twenty-five studies evaluated method performances, 16 of them using the area under the curve (AUC) for a range of positive and negative controls as their main measure. Despite the likelihood that performance measurement could vary by drug-event pair, only 10 studies reported performance stratified by drugs and outcomes, in a heterogeneous manner. The replicability of the performance assessment results was limited due to lack of transparency in reporting and the lack of a gold standard reference set.

CONCLUSIONS

A variety of methods have been described in the literature for signal detection with routinely collected data. No method showed superior performance in all papers and across all drugs and outcomes, performance assessment and reporting were heterogeneous. However, there is limited evidence that self-controlled designs, high dimensional propensity scores, and machine learning can achieve higher performances than other methods.

A medication-wide association study (MWAS) on repurposed drugs for COVID-19 with Pre-pandemic prescription medication exposure and pregnancy outcomes

Information on effects of medication therapies during pregnancy is lacking as pregnant patients are often excluded from clinical trials. This retrospective study explores the potential of using electronic health record (EHR) data to inform safety profiles of repurposed COVID medication therapies on pregnancy outcomes using pre-COVID data. We conducted a medication-wide association study (MWAS) on prescription medication exposures during pregnancy and the risk of cesarean section, preterm birth, and stillbirth, using EHR data between 2010-2017 on deliveries at PennMedicine. Repurposed drugs studied for treatment of COVID-19 were extracted from ClinicalTrials.gov (n = 138). We adjusted for known comorbidities diagnosed within 2 years prior to birth. Using previously developed medication mapping and delivery-identification algorithms, we identified medication exposure in 2,830 of a total 63,334 deliveries; from 138 trials, we found 31 medications prescribed and included in our cohort. We found 21 (68%) of the 31 medications were not positively associated with increased risk of the outcomes examined. With caution, these medications warrant potential for inclusion of pregnant individuals in future studies, while drugs found to be associated with pregnancy outcomes require further investigation. MWAS facilitates hypothesis-driven evaluation of drug safety across all prescription medications, revealing potential drug candidates for further research.

Medication-Wide Association Study Using Electronic Health Record Data of Prescription Medication Exposure and Multifetal Pregnancies: Retrospective Study

Background

Medication-wide association studies (MWAS) have been applied to assess the risk of individual prescription use and a wide range of health outcomes, including cancer, acute myocardial infarction, acute liver failure, acute renal failure, and upper gastrointestinal ulcers. Current literature on the use of preconception and periconception medication and its association with the risk of multiple gestation pregnancies (eg, monozygotic and dizygotic) is largely based on assisted reproductive technology (ART) cohorts. However, among non-ART pregnancies, it is unknown whether other medications increase the risk of multifetal pregnancies.

Objective

This study aimed to investigate the risk of multiple gestational births (eg, twins and triplets) following preconception and periconception exposure to prescription medications in patients who delivered at Penn Medicine.

Methods

We used electronic health record data between 2010 and 2017 on patients who delivered babies at Penn Medicine, a health care system in the Greater Philadelphia area. We explored 3 logistic regression models: model 1 (no adjustment); model 2 (adjustment for maternal age); and model 3—our final logistic regression model (adjustment for maternal age, ART use, and infertility diagnosis). In all models, multiple births (MBs) were our outcome of interest (binary outcome), and each medication was assessed separately as a binary variable. To assess our MWAS model performance, we defined ART medications as our gold standard, given that these medications are known to increase the risk of MB.

Results

Of the 63,334 distinct deliveries in our cohort, only 1877 pregnancies (2.96%) were prescribed any medication during the preconception and first trimester period. Of the 123 medications prescribed, we found 26 (21.1%) medications associated with MB (using nominal P values) and 10 (8.1%) medications associated with MB (using Bonferroni adjustment) in fully adjusted model 3. We found that our model 3 algorithm had an accuracy of 85% (using nominal P values) and 89% (using Bonferroni-adjusted P values).

Conclusions

Our work demonstrates the opportunities in applying the MWAS approach with electronic health record data to explore associations between preconception and periconception medication exposure and the risk of MB while identifying novel candidate medications for further study. Overall, we found 3 novel medications linked with MB that could be explored in further work; this demonstrates the potential of our method to be used for hypothesis generation.

Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)

BACKGROUND

Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias.

OBJECTIVE

With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline.

METHODS

We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses.

RESULTS

A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively.

CONCLUSIONS

Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.

Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits

The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits  to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising > 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits. 'Physical activity' and 'General Health' were the exposure categories containing the highest number of 'tentative signals' in analyses assessing the average association of lifestyle variables, while 'Tobacco use' was the top category for the 10-year change association analyses. Eleven modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to the domains: (i) Smoking, (ii) Beverage (filtered coffee), (iii) physical activity, (iv) alcohol intake, and (v) specific variables related to Nordic lifestyle (hunting/fishing during leisure time and boiled coffee consumption). We used an agnostic, data-driven approach to assess a wide range of established and novel risk factors for cardiometabolic disease. Our findings highlight key variables, along with their respective effect estimates, that might be prioritised for subsequent prediction models and lifestyle interventions.

Drug prescriptions and dementia incidence: a medication-wide association study of 17000 dementia cases among half a million participants

BACKGROUND

Previous studies have suggested that some medications may influence dementia risk. We conducted a hypothesis-generating medication-wide association study to investigate systematically the association between all prescription medications and incident dementia.

METHODS

We used a population-based cohort within the Secure Anonymised Information Linkage (SAIL) databank, comprising routinely-collected primary care, hospital admissions and mortality data from Wales, UK. We included all participants born after 1910 and registered with a SAIL general practice at ≤60 years old. Follow-up was from each participant's 60th birthday to the earliest of dementia diagnosis, deregistration from a SAIL general practice, death or the end of 2018. We considered participants exposed to a medication if they received ≥1 prescription for any of 744 medications before or during follow-up. We adjusted for sex, smoking and socioeconomic status. The outcome was any all-cause dementia code in primary care, hospital or mortality data during follow-up. We used Cox regression to calculate hazard ratios and Bonferroni-corrected p values.

RESULTS

Of 551 344 participants, 16 998 (3%) developed dementia (median follow-up was 17 years for people who developed dementia, 10 years for those without dementia). Of 744 medications, 221 (30%) were associated with dementia. Of these, 217 (98%) were associated with increased dementia incidence, many clustering around certain indications. Four medications (all vaccines) were associated with a lower dementia incidence.

CONCLUSIONS

Almost a third of medications were associated with dementia. The clustering of many drugs around certain indications may provide insights into early manifestations of dementia. We encourage further investigation of hypotheses generated by these results.

Optimizing drug selection from a prescription trajectory of one patient

It is unknown how sequential drug patterns convey information on a patient's health status and treatment guidelines rarely account for this. Drug-agnostic longitudinal analyses of prescription trajectories in a population-wide setting are needed. In this cohort study, we used 24 years of data (1.1 billion prescriptions) from the Danish prescription registry to model the risk of sequentially redeeming a drug after another. Drug pairs were used to build multistep longitudinal prescription trajectories. These were subsequently used to stratify patients and calculate survival hazard ratios between the stratified groups. The similarity between prescription histories was used to determine individuals' best treatment option. Over the course of 122 million person-years of observation, we identified 9 million common prescription trajectories and demonstrated their predictive power using hypertension as a case. Among patients treated with agents acting on the renin-angiotensin system we identified four groups: patients prescribed angiotensin converting enzyme (ACE) inhibitor without change, angiotensin receptor blockers (ARBs) without change, ACE with posterior change to ARB, and ARB posteriorly changed to ACE. In an adjusted time-to-event analysis, individuals treated with ACE compared to those treated with ARB had lower survival probability (hazard ratio, 0.73 [95% CI, 0.64-0.82]; P < 1 × 10^-16). Replication in UK Biobank data showed the same trends. Prescription trajectories can provide novel insights into how individuals' drug use change over time, identify suboptimal or futile prescriptions and suggest initial treatments different from first line therapies. Observations of this kind may also be important when updating treatment guidelines.

Drug-Wide Association Study (DWAS): Challenges and Opportunities

Cancer risk associations with commonly prescribed medications have been mainly evaluated in hypothesis-driven studies that focus on one drug at a time. Agnostic drug-wide association studies (DWAS) offer an alternative approach to simultaneously evaluate associations between a large number of drugs with one or more cancers using large-scale electronic health records. Although cancer DWAS approaches are promising, a number of challenges limit their applicability. This includes the high likelihood of false positivity; lack of biological considerations; and methodological shortcomings, such as inability to tightly control for confounders. As such, the value of DWAS is currently restricted to hypothesis generation with detected signals needing further evaluation. In this commentary, we discuss those challenges in more detail and summarize the approaches to overcome them by using published cancer DWAS studies, including the accompanied article by Støer and colleagues. Despite current concerns, DWAS future is filled with opportunities for developing innovative analytic methods and techniques that incorporate pharmacology, epidemiology, cancer biology, and genetics.See related article by Støer et al., p. 682.

Validity of observational evidence on putative risk and protective factors: appraisal of 3744 meta-analyses on 57 topics

BACKGROUND

The validity of observational studies and their meta-analyses is contested. Here, we aimed to appraise thousands of meta-analyses of observational studies using a pre-specified set of quantitative criteria that assess the significance, amount, consistency, and bias of the evidence. We also aimed to compare results from meta-analyses of observational studies against meta-analyses of randomized controlled trials (RCTs) and Mendelian randomization (MR) studies.

METHODS

We retrieved from PubMed (last update, November 19, 2020) umbrella reviews including meta-analyses of observational studies assessing putative risk or protective factors, regardless of the nature of the exposure and health outcome. We extracted information on 7 quantitative criteria that reflect the level of statistical support, the amount of data, the consistency across different studies, and hints pointing to potential bias. These criteria were level of statistical significance (pre-categorized according to 10^-6, 0.001, and 0.05 p-value thresholds), sample size, statistical significance for the largest study, 95% prediction intervals, between-study heterogeneity, and the results of tests for small study effects and for excess significance.

RESULTS

3744 associations (in 57 umbrella reviews) assessed by a median number of 7 (interquartile range 4 to 11) observational studies were eligible. Most associations were statistically significant at P < 0.05 (61.1%, 2289/3744). Only 2.6% of associations had P < 10^-6, ≥1000 cases (or ≥20,000 participants for continuous factors), P < 0.05 in the largest study, 95% prediction interval excluding the null, and no large between-study heterogeneity, small study effects, or excess significance. Across the 57 topics, large heterogeneity was observed in the proportion of associations fulfilling various quantitative criteria. The quantitative criteria were mostly independent from one another. Across 62 associations assessed in both RCTs and in observational studies, 37.1% had effect estimates in opposite directions and 43.5% had effect estimates differing beyond chance in the two designs. Across 94 comparisons assessed in both MR and observational studies, such discrepancies occurred in 30.8% and 54.7%, respectively.

CONCLUSIONS

Acknowledging that no gold-standard exists to judge whether an observational association is genuine, statistically significant results are common in observational studies, but they are rarely convincing or corroborated by randomized evidence.

Data-driven assessment, contextualisation and implementation of 134 variables in the risk for type 2 diabetes: an analysis of Lifelines, a prospective cohort study in the Netherlands

AIMS/HYPOTHESIS

We aimed to assess and contextualise 134 potential risk variables for the development of type 2 diabetes and to determine their applicability in risk prediction.

METHODS

A total of 96,534 people without baseline diabetes (372,007 person-years) from the Dutch Lifelines cohort were included. We used a risk variable-wide association study (RV-WAS) design to independently screen and replicate risk variables for 5-year incidence of type 2 diabetes. For identified variables, we contextualised HRs, calculated correlations and assessed their robustness and unique contribution in different clinical contexts using bootstrapped and cross-validated lasso regression models. We evaluated the change in risk, or 'HR trajectory', when sequentially assigning variables to a model.

RESULTS

We identified 63 risk variables, with novel associations for quality-of-life indicators and non-cardiovascular medications (i.e., proton-pump inhibitors, anti-asthmatics). For continuous variables, the increase of 1 SD of HbA_1c, i.e., 3.39 mmol/mol (0.31%), was equivalent in risk to an increase of 0.53 mmol/l of glucose, 19.8 cm of waist circumference, 8.34 kg/m² of BMI, 0.67 mmol/l of HDL-cholesterol, and 0.14 mmol/l of uric acid. Other variables required an increase of >3 SD, which is not physiologically realistic or a rare occurrence in the population. Though moderately correlated, the inclusion of four variables satiated prediction models. Invasive variables, except for glucose and HbA_1c, contributed little compared with non-invasive variables. Glucose, HbA_1c and family history of diabetes explained a unique part of disease risk. Adding risk variables to a satiated model can impact the HRs of variables already in the model.

CONCLUSIONS

Many variables show weak or inconsistent associations with the development of type 2 diabetes, and only a handful can reliably explain disease risk. Newly discovered risk variables will yield little over established factors, and existing prediction models can be simplified. A systematic, data-driven approach to identify risk variables for the prediction of type 2 diabetes is necessary for the practice of precision medicine.

Disease-modifying agents for multiple sclerosis and the risk for reporting cancer: A disproportionality analysis using the US Food and Drug Administration Adverse Event Reporting System database

AIM

While the efficacy of disease-modifying therapies (DMTs) for patients with multiple sclerosis (MS) is established, little is known about their long-term safety. Cancer-risk after DMT use remains unclear. This study aimed to investigate whether the prescription of DMTs for patients with MS increases the risk of reporting cancer.

METHODS

Data from the Food and Drug Administration Adverse Event Reporting System were extracted from 2004 to 2020. After data cleaning, the crude and adjusted reported odds ratios (cROR and aROR) for cancer were calculated for DMTs with Interferon beta-1a as the reference drug. Sensitivity analyses investigated the group of reports with multiple registered DMTs, the effect of indication restriction and the results when using the rest of the DMTs as reference.

RESULTS

For malignant tumours, aROR (95% confidence interval [CI]) values were Cladribine 0.46 (0.18-0.95), Dimethyl fumarate 0.30 (0.27-0.34), Fingolimod 0.61 (0.53-0.70), Glatiramer 0.50 (0.43-0.58), Alemtuzumab 0.84 (0.64-1.08), Interferon beta-1b 0.49 (0.42-0.56), Natalizumab 0.36 (0.34-0.39), Ocrelizumab 0.48 (0.29-0.74), Peginterferon beta-1a 0.35 (0.26-0.48), Siponimod 0.89 (0.47-1.54) and Teriflunomide 0.25(0.21-0.30) adjusted to age, gender and concomitant medications. In the sensitivity analysis, when the rest of the drugs were used as a reference, Interferon beta-1a and Peginterferon beta-1a had aROR (95% CI) 2.60 (2.47-2.74, P < .001) and Alemtuzumab had aROR 1.47 (1.13-1.88, I = .003).

CONCLUSIONS

No safety signal for increased cancer risk was detected among the approved DMTs. A potential safety signal detected in the sensitivity analysis concerning Interferon beta-1a and Alemtuzumab requires further evaluation with more robust evidence.

Drug Use and Cancer Risk: A Drug-Wide Association Study (DWAS) in Norway

BACKGROUND

Population-based pharmaco-epidemiologic studies are used to assess postmarketing drug safety and discover beneficial effects of off-label drug use. We conducted a drug-wide association study (DWAS) to screen for associations between prescription drugs and cancer risk.

METHODS

This registry-based, nested case-control study, 1:10 matched on age, sex, and date of diagnosis of cases, comprises approximately 2 million Norwegian residents, including their drug history from 2004 to 2014. We evaluated the association between prescribed drugs, categorized according to the anatomical therapeutic chemical (ATC) classification system, and the risk of the 15 most common cancer types, overall and by histology. We used stratified Cox regression, adjusted for other drug use, comorbidity, county, and parity, and explored dose-response trends.

RESULTS

We found 145 associations among 1,230 drug-cancer combinations on the ATC2-level and 77 of 8,130 on the ATC4-level. Results for all drug-cancer combinations are presented in this article and an online tool (https://pharmacoepi.shinyapps.io/drugwas/). Some associations have been previously reported, that is, menopausal hormones and breast cancer risk, or are likely confounded, that is, chronic obstructive pulmonary diseases and lung cancer risk. Other associations were novel, that is, inverse association between proton pump inhibitors and melanoma risk, and carcinogenic association of propulsives and lung cancer risk.

CONCLUSIONS

This study confirmed previously reported associations and generated new hypotheses on possible carcinogenic or chemopreventive effects of prescription drugs. Results from this type of explorative approach need to be validated in tailored epidemiologic and preclinical studies.

IMPACT

DWAS studies are robust and important tools to define new drug-cancer hypotheses.See related commentary by Wang and Gadalla, p. 597.

A systematic assessment of the association between frequently prescribed medicines and the risk of common cancers: a series of nested case-control studies

BACKGROUND

Studies systematically screening medications have successfully identified prescription medicines associated with cancer risk. However, adjustment for confounding factors in these studies has been limited. We therefore investigated the association between frequently prescribed medicines and the risk of common cancers adjusting for a range of confounders.

METHODS

A series of nested case-control studies were undertaken using the Primary Care Clinical Informatics Unit Research (PCCIUR) database containing general practice (GP) records from Scotland. Cancer cases at 22 cancer sites, diagnosed between 1999 and 2011, were identified from GP records and matched with up to five controls (based on age, gender, GP practice and date of registration). Odds ratios (OR) and 95% confidence intervals (CI) comparing any versus no prescriptions for each of the most commonly prescribed medicines, identified from prescription records, were calculated using conditional logistic regression, adjusting for comorbidities. Additional analyses adjusted for smoking use. An association was considered a signal based upon the magnitude of its adjusted OR, p-value and evidence of an exposure-response relationship. Supplementary analyses were undertaken comparing 6 or more prescriptions versus less than 6 for each medicine.

RESULTS

Overall, 62,109 cases and 276,580 controls were included in the analyses and a total of 5622 medication-cancer associations were studied across the 22 cancer sites. After adjusting for comorbidities 2060 medicine-cancer associations for any prescription had adjusted ORs greater than 1.25 (or less than 0.8), 214 had a corresponding p-value less than or equal to 0.01 and 118 had evidence of an exposure-dose relationship hence meeting the criteria for a signal. Seventy-seven signals were identified after additionally adjusting for smoking. Based upon an exposure of 6 or more prescriptions, there were 118 signals after adjusting for comorbidities and 82 after additionally adjusting for smoking.

CONCLUSIONS

In this study a number of novel associations between medicine and cancer were identified which require further clinical and epidemiological investigation. The majority of medicines were not associated with an altered cancer risk and many identified signals reflected known associations between medicine and cancer.

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets

Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of individuals, almost any desired relationship can be inferred with a suitable combination of covariates or analytic choices. Inspired by the genome-wide association study analysis paradigm that has transformed human genetics, X-wide association studies or "XWAS" have emerged as a popular approach to systematically analyzing nongenetic data sets and guarding against false positives. However, these studies often yield hundreds or thousands of associations characterized by modest effect sizes and miniscule P values. Many of these associations will be spurious and emerge due to confounding and other biases. One way of characterizing confounding in the genomics paradigm is the genomic inflation factor. An analogous "X-wide inflation factor," denoted λX, can be defined and applied to published XWAS. Effects that arise in XWAS may be prioritized using replication, triangulation, quantification of measurement error, contextualization of each effect in the distribution of all effect sizes within a field, and pre-registration. Criteria like those of Bradford Hill need to be reconsidered in light of exposure-wide epidemiology to prioritize signals among signals.

Identification of potential carcinogenic and chemopreventive effects of prescription drugs: a protocol for a Norwegian registry-based study

INTRODUCTION

Surveillance of unintended effects of pharmaceuticals (pharmacovigilance or drug safety) is crucial, as knowledge of rare or late side effects is limited at the time of the introduction of new medications into the market. Side effects of drugs may involve increased or decreased risk of cancer, but these typically appear after a long induction period. This fact, together with low incidences of many cancer types, limits the usefulness of traditional pharmacovigilance strategies, primarily based on spontaneous reporting of adverse events, to identify associations between drug use and cancer risk. Postmarketing observational pharmacoepidemiological studies are therefore crucial in the evaluation of drug-cancer associations.

METHODS AND ANALYSIS

The main data sources in this project will be the Norwegian Prescription Database and the Cancer Registry of Norway. The underlying statistical model will be based on a multiple nested case-control design including all adult (~200 000) incident cancer cases within the age-range 18-85 years from 2007 through 2015 in Norway as cases. 10 cancer-free population controls will be individually matched to these cases with respect to birth year, sex and index date (date of cancer diagnosis). Drug exposure will be modelled as chronic user/non-user by counting prescriptions, and cumulative use by summarising all dispensions' daily defined doses over time. Conditional logistic regression models adjusted for comorbidity (National Patient Register), socioeconomic parameters (Statistics Norway), concomitant drug use and, for female cancers, reproduction data (Medical Birth Registry), will be applied to identify drug-use-cancer-risk associations.

ETHICS AND DISSEMINATION

The study is approved by the regional ethical committee and the Norwegian data protection authority. Results of the initial screening step and analysis pipeline will be described in a key paper. Subsequent papers will report the evaluation of identified signals in replication studies. Results will be published in peer-reviewed journals, at scientific conferences and through press releases.

Perspective: Limiting Dependence on Nonrandomized Studies and Improving Randomized Trials in Human Nutrition Research: Why and How

A large majority of human nutrition research uses nonrandomized observational designs, but this has led to little reliable progress. This is mostly due to many epistemologic problems, the most important of which are as follows: difficulty detecting small (or even tiny) effect sizes reliably for nutritional risk factors and nutrition-related interventions; difficulty properly accounting for massive confounding among many nutrients, clinical outcomes, and other variables; difficulty measuring diet accurately; and suboptimal research reporting. Tiny effect sizes and massive confounding are largely unfixable problems that narrowly confine the scenarios in which nonrandomized observational research is useful. Although nonrandomized studies and randomized trials have different priorities (assessment of long-term causality compared with assessment of treatment effects), the odds for obtaining reliable information with the former are limited. Randomized study designs should therefore largely replace nonrandomized studies in human nutrition research going forward. To achieve this, many of the limitations that have traditionally plagued most randomized trials in nutrition, such as small sample size, short length of follow-up, high cost, and selective reporting, among others, must be overcome. Pivotal megatrials with tens of thousands of participants and lifelong follow-up are possible in nutrition science with proper streamlining of operational costs. Fixable problems that have undermined observational research, such as dietary measurement error and selective reporting, need to be addressed in randomized trials. For focused questions in which dietary adherence is important to maximize, trials with direct observation of participants in experimental in-house settings may offer clean answers on short-term metabolic outcomes. Other study designs of randomized trials to consider in nutrition include registry-based designs and "N-of-1" designs. Mendelian randomization designs may also offer some more reliable leads for testing interventions in trials. Collectively, an improved randomized agenda may clarify many things in nutrition science that might never be answered credibly with nonrandomized observational designs.

Yale school of public health symposium on lifetime exposures and human health: the exposome; summary and future reflections

The exposome is defined as "the totality of environmental exposures encountered from birth to death" and was developed to address the need for comprehensive environmental exposure assessment to better understand disease etiology. Due to the complexity of the exposome, significant efforts have been made to develop technologies for longitudinal, internal and external exposure monitoring, and bioinformatics to integrate and analyze datasets generated. Our objectives were to bring together leaders in the field of exposomics, at a recent Symposium on "Lifetime Exposures and Human Health: The Exposome," held at Yale School of Public Health. Our aim was to highlight the most recent technological advancements for measurement of the exposome, bioinformatics development, current limitations, and future needs in environmental health. In the discussions, an emphasis was placed on moving away from a one-chemical one-health outcome model toward a new paradigm of monitoring the totality of exposures that individuals may experience over their lifetime. This is critical to better understand the underlying biological impact on human health, particularly during windows of susceptibility. Recent advancements in metabolomics and bioinformatics are driving the field forward in biomonitoring and understanding the biological impact, and the technological and logistical challenges involved in the analyses were highlighted. In conclusion, further developments and support are needed for large-scale biomonitoring and management of big data, standardization for exposure and data analyses, bioinformatics tools for co-exposure or mixture analyses, and methods for data sharing.

Ethics and Epistemology in Big Data Research

Biomedical innovation and translation are increasingly emphasizing research using "big data." The hope is that big data methods will both speed up research and make its results more applicable to "real-world" patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals.