Traditional Observational Studies Research Articles

Does Chronic Intestinal Inflammation Promote Atrial Fibrillation: A Mendelian Randomization Study With Populations of European Ancestry.

Background: Inflammatory bowel disease (IBD), comprising ulcerative colitis (UC), and Crohn's disease (CD), has been reported to be associated with an increased risk of atrial fibrillation (AF). However, the causal role of the chronic intestinal inflammation (CII) in the development of AF remains controversial. We use Mendelian randomization (MR) analysis to explore the causal inference of CII on AF.Methods: A two-sample MR analysis was performed to estimate the potential causal effect of CII on AF. Statistical summaries for the associations between single nucleotide polymorphisms (SNPs) and phenotypes of CII were obtained from genome-wide association studies (GWAS) with cohorts of CD (n = 51,874), UC (n = 47,745), and IBD (n = 65,642) of European descent. The GWAS of 1,030,836 people of European ancestry, including 60,620 AF cases and 970,216 controls was collected to identify genetic variants underlying AF. The causal inference was estimated using the multiplicative random effects inverse-variance weighted method (IVW). The methods of MR-Egger, simple median, and weighted median were also employed to avoid the bias of pleiotropy effects.Results: Using three sets of SNPs (75 SNPs of CD, 60 SNPs of UC, and 95 SNPs of IBD), multiplicative random-effect IVW model estimated a universal null effect of CII on AF (CD: OR = 1.0059, 95% CI: 0.9900, 1.0220, p = 0.47; UC: OR = 1.0087, 95% CI: 0.9896, 1.0281, p = 0.38; IBD: OR = 1.0080, 95% CI: 0.9908, 1.0255, p = 0.37). Similar results were observed using the MR-Egger, simple median, weighted median methods.Conclusion: As opposing to the traditional observational studies, our two-sample MR analysis did not find enough evidence to support a causal role of either CD or UC in the development of AF.

Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression.

Background:Type 2 diabetes (T2DM) and Parkinson’s disease (PD) are prevalent diseases that affect an aging population. Previous systematic reviews and meta-analyses have explored the relationship between diabetes and the risk of PD, but the results have been conflicting.Objective:The objective was to investigate T2DM as a determinant of PD through a meta-analysis of observational and genetic summary data.Methods:A systematic review and meta-analysis of observational studies was undertaken by searching 6 databases. We selected the highest-quality studies investigating the association of T2DM with PD risk and progression. We then used Mendelian randomization (MR) to investigate the causal effects of genetic liability toward T2DM on PD risk and progression, using summary data derived from genome-wide association studies.Results:In the observational part of the study, pooled effect estimates showed that T2DM was associated with an increased risk of PD (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.07–1.36), and there was some evidence that T2DM was associated with faster progression of motor symptoms (standardized mean difference [SMD] 0.55, 95% CI 0.39–0.72) and cognitive decline (SMD −0.92, 95% CI −1.50 to −0.34). Using MR, we found supportive evidence for a causal effect of diabetes on PD risk (inverse-variance weighted method [IVW] OR 1.08, 95% CI 1.02–1.14; P = 0.010) and some evidence of an effect on motor progression (IVW OR 1.10, 95% CI 1.01–1.20; P = 0.032) but not on cognitive progression.Conclusions:Using meta-analyses of traditional observational studies and genetic data, we observed convincing evidence for an effect of T2DM on PD risk and new evidence to support a role in PD progression.

Body mass index and colorectal cancer risk: A Mendelian randomization study

Traditional observational studies have reported a positive association between higher body mass index (BMI) and the risk of colorectal cancer (CRC). However, evidence from other approaches to pursue the causal relationship between BMI and CRC is sparse. A two‐sample Mendelian randomization (MR) study was undertaken using 68 single nucleotide polymorphisms (SNPs) from the Japanese genome‐wide association study (GWAS) and 654 SNPs from the GWAS catalogue for BMI as sets of instrumental variables. For the analysis of SNP‐BMI associations, we undertook a meta‐analysis with 36 303 participants in the Japanese Consortium of Genetic Epidemiology studies (J‐CGE), comprising normal populations. For the analysis of SNP‐CRC associations, we utilized 7636 CRC cases and 37 141 controls from five studies in Japan, and undertook a meta‐analysis. Mendelian randomization analysis of inverse‐variance weighted method indicated that a one‐unit (kg/m2) increase in genetically predicted BMI was associated with an odds ratio of 1.13 (95% confidence interval, 1.06‐1.20; P value <.001) for CRC using the set of 68 SNPs, and an odds ratio of 1.07 (1.03‐1.11, 0.001) for CRC using the set of 654 SNPs. Sensitivity analyses robustly showed increased odds ratios for CRC for every one‐unit increase in genetically predicted BMI. Our MR analyses strongly support the evidence that higher BMI influences the risk of CRC. Although Asians are generally leaner than Europeans and North Americans, avoiding higher BMI seems to be important for the prevention of CRC in Asian populations.

Dialoguing with Data and Data Reduction: An Observational, Narrowing-Down Approach to Social Media Network Analysis

In this article, we propose an observational, narrowing-down approach to analysing social media networks and developing research design by the joint use of computational algorithms and researchers’ inductive exploration and interpretive explanations. The Brexit referendum on Twitter study is used to illustrate how we applied this approach in practice. In this study, observation helped us combine the strengths of computational statistical analysis and modelling and of inductive inquiries. Computational algorithms and tools including Elasticsearch, Kibana and Gephi provided us with an “ethnographic field” where we were able to inductively observe the relationships among users and to reduce the amount of data down to a level in which we could intuitively understand these relationships. In traditional observational studies, talking to human subjects and observing their interactions in a research site are important to ethnographers. Likewise, it is useful for social science researchers to dialogue with data, observe human relationships embodied in the data and reconstructed by computational tools, and understand these relationships through closely examining a small batch of meaningful data that is extracted from large-scale data. In this case study, adopting the proposed approach, we found the importance of political disagreement leading to a tale of two politicians, in which pro-Brexit users denounced @David_Cameron but legitimised @Nigel_Farage.

Mendelian randomization as a tool for causal inference in human nutrition and metabolism.

The current review describes the fundamentals of the Mendelian randomization framework and its current application for causal inference in human nutrition and metabolism. In the Mendelian randomization framework, genetic variants that are strongly associated with the potential risk factor are used as instrumental variables to determine whether the risk factor is a cause of the disease. Mendelian randomization studies are less susceptible to confounding and reverse causality compared with traditional observational studies. The Mendelian randomization study design has been increasingly used in recent years to appraise the causal associations of various nutritional factors, such as milk and alcohol intake, circulating levels of micronutrients and metabolites, and obesity with risk of different health outcomes. Mendelian randomization studies have confirmed some but challenged other nutrition-disease associations recognized by traditional observational studies. Yet, the causal role of many nutritional factors and intermediate metabolic changes for health and disease remains unresolved. Mendelian randomization can be used as a tool to improve causal inference in observational studies assessing the role of nutritional factors and metabolites in health and disease. There is a need for more large-scale genome-wide association studies to identify more genetic variants for nutritional factors that can be utilized for Mendelian randomization analyses.

Abstract 3486: Body mass index and colorectal cancer risk in Japanese populations: a Mendelian randomization study

Abstract Background: Prospective cohort studies have shown a positive association between body mass index (BMI) and the risk of colorectal cancer (CRC). However, limitations inherent in traditional observational studies, such as reverse causality and residual confounding, might explain the association. To overcome these limitations, Mendelian randomization (MR) studies of the BMI-CRC association have been conducted in European and U.S. groups, but the association remains to be clarified in East Asians. Purpose: We performed MR analyses to investigate the causal association between BMI and CRC in Japanese populations. Methods: Our study design consisted of 4 steps. (1) Based on a previous Genome-Wide Association Study in Japanese populations, we selected 68 BMI-associated single nucleotide polymorphisms (SNPs), which explained about 2.0% of the BMI variance, as instruments. (2) We examined the associations between 68 SNPs and BMI among general Japanese populations in the Japanese Consortium of Genetic Epidemiology studies (N=36,253). (3) We performed a fixed-effect meta-analysis to investigate associations between 68 SNPs and CRC using individual-level data and publicly available summary-statistic data of Japanese populations (cases=7,473, controls=33,322). (4) Finally, we used the inverse-variance weighted (IVW) method to calculate MR estimates. Several sensitivity analyses were applied to assess robustness or horizontal pleiotropy using weighted median, weighted mode, MR-Egger regression, and MR-Pleiotropy Residual Sum and Outlier (PRESSO) methods. Results: In the main analysis using the IVW method, a one-unit increase in BMI was associated with an odds ratio of 1.12 (95% confidence interval [CI]: 1.06-1.20) for CRC. Sensitivity analyses consistently showed increased odds ratios for CRC per one-unit increase in BMI. The odds ratios for weighted median, weighted mode, and MR-Egger regression were 1.16 (95% CI: 1.06-1.27), 1.14 (95% CI: 1.05-1.24), and 1.10(95% CI: 0.98-1.23), respectively. The MR-Egger intercept P-value was 0.63. No outlier was detected using the MR-PRESSO method. Conclusions: Our MR analyses provide evidence that BMI is positively associated with CRC in Japanese populations. Our findings seem to suggest that MR estimates for the BMI-CRC association may be consistent across different ethnicities. Citation Format: Shiori Suzuki, Atsushi Goto, Masahiro Nakatochi, Akira Narita, Taiki Yamaji, Norie Sawada, Ryoko Katagiri, Tsuyoshi Hachiya, Yoichi Sutoh, Isao Oze, Yuriko Koyanagi, Yumiko Kasugai, Hidemi Ito, Hiroaki Ikezaki, Keitaro Tanaka, Takashi Tamura, Haruo Mikami, Toshiro Takezaki, Sadao Suzuki, Nagato Kuriyama, Kiyonori Kuriki, Yoshikuni Kita, Kokichi Arisawa, Kenji Takeuchi, Kozo Tanno, Atsushi Shimizu, Gen Tamiya, Atsushi Hozawa, Kengo Kinoshita, Kenji Wakai, Makoto Sasaki, Masayuki Yamamoto, Keitaro Matsuo, Shoichiro Tsugane, Motoki Iwasaki. Body mass index and colorectal cancer risk in Japanese populations: a Mendelian randomization study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3486.

An Appraisal of the Role of Previously Reported Risk Factors in the Age at Menopause Using Mendelian Randomization.

ObjectiveMenopause at a young age is associated with many health problems in women, including osteoporosis, depressive symptoms, coronary disease, and stroke. Many traditional observational studies have reported some potential risk factors for early menopause but have drawn different conclusions. This inconsistency can be attributed mainly to unmodified confounding factors. Identifying the factors causally associated with age at menopause is important for early intervention in women with abnormal menopause timing, and for improving the quality of life for postmenopausal women. This study aims to appraise whether the previously reported risk factors are causally associated with early age at natural menopause (ANM) susceptibility.MethodsWe used Mendelian randomization, a statistical method wherein genetic variants are used to determine whether an observational association between a risk factor and an outcome is consistent with a causal effect.ResultsWomen with earlier age at menarche (β = 0.34, se = 0.16, p = 0.035), lower education level (β = 1.19, se = 0.41, p = 0.004) and higher body mass index (β = −0.05, se = 0.02, p = 0.027) had greater risk for early ANM. The causal link between early age at menarche and early ANM was replicated using ReproGen consortium data (β = 0.23, se = 0.07, p = 0.001). However, a current smoking habit, one of previously reported risk factors, was less likely to be correlated causally with early ANM, suggesting that previous observational studies may not have sufficiently adjusted for confounders.ConclusionOur results help to identify the risk factors of ANM via a genetics approach and future research into the biological mechanism could further help with targeted prevention for early menopause.

Multimodal mental health analysis in social media.

Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.

Application of Big Data analysis in gastrointestinal research.

Big Data, which are characterized by certain unique traits like volume, velocity and value, have revolutionized the research of multiple fields including medicine. Big Data in health care are defined as large datasets that are collected routinely or automatically, and stored electronically. With the rapidly expanding volume of health data collection, it is envisioned that the Big Data approach can improve not only individual health, but also the performance of health care systems. The application of Big Data analysis in the field of gastroenterology and hepatology research has also opened new research approaches. While it retains most of the advantages and avoids some of the disadvantages of traditional observational studies (case-control and prospective cohort studies), it allows for phenomapping of disease heterogeneity, enhancement of drug safety, as well as development of precision medicine, prediction models and personalized treatment. Unlike randomized controlled trials, it reflects the real-world situation and studies patients who are often under-represented in randomized controlled trials. However, residual and/or unmeasured confounding remains a major concern, which requires meticulous study design and various statistical adjustment methods. Other potential drawbacks include data validity, missing data, incomplete data capture due to the unavailability of diagnosis codes for certain clinical situations, and individual privacy. With continuous technological advances, some of the current limitations with Big Data may be further minimized. This review will illustrate the use of Big Data research on gastrointestinal and liver diseases using recently published examples.

S190. SCHIZOTYPY &amp; SUICIDALITY: A MENDELIAN RANDOMISATION ANALYSIS

BackgroundSubclinical psychotic symptoms, known as schizotypy, predict concurrent and future suicidal ideation and acts. Some suggest this relationship reflects the influence of shared environmental risk factors, or that schizotypy and suicidality are both non-specific indicators of severity of psychopathology. However, this artefactual explanation does not account for the heritability of environmental risk factors, the link between schizotypy and more severe expressions of suicidality, or contemporary theories of suicidality.MethodsWe tested whether schizotypy has a direct (causal) effect on the development of suicidal thoughts using a Mendelian randomisation analysis to avoid problems of reverse causality, confounding, and measurement error associated with traditional observational studies. In Mendelian randomisation analyses, genetic variants are used as a proxy measure for a phenotype in order to make causal inferences about the effect of the phenotype on an outcome. We used a schizophrenia gene risk score (GRS), a measure of schizophrenia liability, as a proxy measure for schizotypy. Participants (n = 4767) were part of the Philadelphia Neurodevelopmental Cohort, a publicly available resource designed to assess behavioural and biological factors contributing to mental illness in young adults (aged 8–21). Regression analyses were used to test relationships.ResultsSchizotypy was found to be a strong predictor of both passive (OR = 1.84, p < .001) and active (OR = 2.69, p < .001) suicidal ideation. No relationship was found between the schizophrenia GRS and schizotypy when analysed in separate ethnic groups to adjust for population stratification (European American; B = .18, p = .708, African American; B = .086, p = .303). No relationship was found between the schizophrenia GRS and passive (OR = .97, p = .721) or active (OR = .99, p = .778) suicidal ideation.DiscussionThe hypothesis that there is a causal relationship between schizotypy and suicidality was not supported, though it is unclear if this is due to the schizophrenia GRS being a poor proxy for schizotypy, or a true absence of a causal relationship. Understanding causal risk factors for suicidality is a key area of research and future research should continue to address this using genetically-sensitive designs.

Comparison of Survey Modality and Response Rate in Dermatologists’ Perceptions and Opinions of Sunscreens

Background. Survey instruments are valuable tools for research and provide insight into real-world practice and areas of knowledge deficits in ways that traditional observational studies and randomized trials cannot. Despite some experts espousing survey response rates ≥60% as valid, there is no consensus as to what an adequate response rate is for a scientific survey. Furthermore, little is known what effect the interaction of survey administration modality and response rate has on results. Objective. To compare the results of differing survey modalities (which typically feature different response rate ranges). Methods. A validated, 21-item survey assessing perceptions of, recommendations regarding, and usage of sunscreen was distributed to three samples of dermatologists using three different modalities: pen/paper via mail, online via email, and in real-time via an audience response system at a national conference. Results. Response rates varied widely by survey modality (30% mail, 9% email 95% live). However, dermatologists’ responses to individual survey questions were largely consistent across modalities, with a statistically significant difference seen for only three questions (recommending sunscreens based on cosmetic elegance, recommending sunscreens based on photostability, and recommending vitamin D supplementation as a means to avoid sun exposure. Conclusions. In this study evaluating dermatologists’ perceptions of, recommendations for, and personal use of sunscreen, survey results were largely consistent across three different modalities (mail, email, live) despite widely variable response rates, from 9% to 95%. These results suggest that when a scientific survey sample is representative of the target population, minimum response rate and survey modality appear to have negligible impact on results.

Optimizing research in symptomatic uterine fibroids with development of a computable phenotype for use with electronic health records

Women with symptomatic uterine fibroids can report a myriad of symptoms, including pain, bleeding, infertility, and psychosocial sequelae. Optimizing fibroid research requires the ability to enroll populations of women with image-confirmed symptomatic uterine fibroids. Our objective was to develop an electronic health record-based algorithm to identify women with symptomatic uterine fibroids for a comparative effectiveness study of medical or surgical treatments on quality-of-life measures. Using an iterative process and text-mining techniques, an effective computable phenotype algorithm, composed of demographics, and clinical and laboratory characteristics, was developed with reasonable performance. Such algorithms provide a feasible, efficient way to identify populations of women with symptomatic uterine fibroids for the conduct of large traditional or pragmatic trials and observational comparative effectiveness studies. Symptomatic uterine fibroids, due to menorrhagia, pelvic pain, bulk symptoms, or infertility, are a source of substantial morbidity for reproductive-age women. Comparing Treatment Options for Uterine Fibroids is a multisite registry study to compare the effectiveness of hormonal or surgical fibroid treatments on women's perceptions of their quality of life. Electronic health record-based algorithms are able to identify large numbers of women with fibroids, but additional work is needed to develop electronic health record algorithms that can identify women with symptomatic fibroids to optimize fibroid research. We sought to develop an efficient electronic health record-based algorithm that can identify women with symptomatic uterine fibroids in a large health care system for recruitment into large-scale observational and interventional research in fibroid management. We developed and assessed the accuracy of 3 algorithms to identify patients with symptomatic fibroids using an iterative approach. The data source was the Carolina Data Warehouse for Health, a repository for the health system's electronic health record data. In addition to International Classification of Diseases, Ninth Revision diagnosis and procedure codes and clinical characteristics, text data-mining software was used to derive information from imaging reports to confirm the presence of uterine fibroids. Results of each algorithm were compared with expert manual review to calculate the positive predictive values for each algorithm. Algorithm 1 was composed of the following criteria: (1) age 18-54 years; (2) either ≥1 International Classification of Diseases, Ninth Revision diagnosis codes for uterine fibroids or mention of fibroids using text-mined key words in imaging records or documents; and (3) no International Classification of Diseases, Ninth Revision or Current Procedural Terminology codes for hysterectomy and no reported history of hysterectomy. The positive predictive value was 47% (95% confidence interval 39-56%). Algorithm 2 required ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids and positive text-mined key words and had a positive predictive value of 65% (95% confidence interval 50-79%). In algorithm 3, further refinements included ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids on separate outpatient visit dates, the exclusion of women who had a positive pregnancy test within 3 months of their fibroid-related visit, and exclusion of incidentally detected fibroids during prenatal or emergency department visits. Algorithm 3 achieved a positive predictive value of 76% (95% confidence interval 71-81%). An electronic health record-based algorithm is capable of identifying cases of symptomatic uterine fibroids with moderate positive predictive value and may be an efficient approach for large-scale study recruitment.

Education and coronary heart disease: mendelian randomisation study

Objective To determine whether educational attainment is a causal risk factor in the development of coronary heart disease.Design Mendelian randomisation study, using genetic data as proxies for education to minimise...

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media.

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.

Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines.

BackgroundEstimating the effectiveness of meningococcal vaccines with high accuracy and precision can be challenging due to the low incidence of the invasive disease, which ranges between 0.5 and 1 cases per 100,000 in Europe and North America. Vaccine effectiveness (VE) is usually estimated with a screening method that combines in one formula the proportion of meningococcal disease cases that have been vaccinated and the proportion of vaccinated in the overall population. Due to the small number of cases, initial point estimates are affected by large uncertainties and several years may be required to estimate VE with a small confidence interval.MethodsWe used a Monte Carlo maximum likelihood (MCML) approach to estimate the effectiveness of meningococcal vaccines, based on stochastic simulations of a dynamic model for meningococcal transmission and vaccination. We calibrated the model to describe two immunization campaigns: the campaign against MenC in England and the Bexsero campaign that started in the UK in September 2015. First, the MCML method provided estimates for both the direct and indirect effects of the MenC vaccine that were validated against results published in the literature. Then, we assessed the performance of the MCML method in terms of time gain with respect to the screening method under different assumptions of VE for Bexsero.ResultsMCML estimates of VE for the MenC immunization campaign are in good agreement with results based on the screening method and carriage studies, yet characterized by smaller confidence intervals and obtained using only incidence data collected within 2 years of scheduled vaccination. Also, we show that the MCML method could provide a fast and accurate estimate of the effectiveness of Bexsero, with a time gain, with respect to the screening method, that could range from 2 to 15 years, depending on the value of VE measured from field data.ConclusionsResults indicate that inference methods based on dynamic computational models can be successfully used to quantify in near real time the effectiveness of immunization campaigns against Neisseria meningitidis. Such an approach could represent an important tool to complement and support traditional observational studies, in the initial phase of a campaign.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0642-2) contains supplementary material, which is available to authorized users.

Mendelian randomisation - a genetic approach to an epidemiological method

BACKGROUND Genetic information is becoming more easily available, and rapid progress is being made in developing methods of illuminating issues of interest. Mendelian randomisation makes it possible to study causes of disease using observational data. The name refers to the random distribution of gene variants in meiosis. The methodology makes use of genes that influence a risk factor for a disease, without influencing the disease itself. In this review article I explain the principles behind Mendelian randomisation and present the areas of application for this methodology.MATERIAL AND METHOD Methodology articles describing Mendelian randomisation were reviewed. The articles were found through a search in PubMed with the combination «mendelian randomization» OR «mendelian randomisation», and a search in McMaster Plus with the combination «mendelian randomization». A total of 15 methodology articles were read in full text. Methodology articles were supplemented by clinical studies found in the PubMed search.RESULTS In contrast to traditional observational studies, Mendelian randomisation studies are not affected by two important sources of error: conventional confounding variables and reverse causation. Mendelian randomisation is therefore a promising tool for studying causality. Mendelian randomisation studies have already provided valuable knowledge on the risk factors for a wide range of diseases. It is nevertheless important to be aware of the limitations of the methodology. As a result of the rapid developments in genetics research, Mendelian randomisation will probably be widely used in future years.INTERPRETATION If Mendelian randomisation studies are conducted correctly, they may help to reveal both modifiable and non-modifiable causes of disease.

Mendelian Randomization - the Key to Understanding Aspects of Parkinson's Disease Causation?

Parkinson's disease has multiple determinants and is associated with a wide range of exposures that appear to modify risk in traditional observational studies, including numerous lifestyle and environmental factors. Across other fields of medicine, Mendelian randomization has emerged as a powerful method to examine whether associations between exposures and disease outcomes are causal. Here we discuss the concept of Mendelian randomization, its potential relevance to Parkinson's disease, and suggest avenues through which the method could be employed to further understanding of the causal basis of Parkinson's disease. © 2015 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

The utility of web mining for epidemiological research: studying the association between parity and cancer risk

The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored. Using advanced web crawling and tailored information extraction procedures, the authors automatically collected and analyzed the text content of 79 394 online obituary articles published between 1998 and 2014. The collected data included 51 911 cancer (27 330 breast; 9470 lung; 6496 pancreatic; 6342 ovarian; 2273 colon) and 27 483 non-cancer cases. With the derived information, the authors replicated a case-control study design to investigate the association between parity (i.e., childbearing) and cancer risk. Age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies. Parity was found to be associated with a significantly reduced risk of breast cancer (OR = 0.78, 95% CI, 0.75-0.82), pancreatic cancer (OR = 0.78, 95% CI, 0.72-0.83), colon cancer (OR = 0.67, 95% CI, 0.60-0.74), and ovarian cancer (OR = 0.58, 95% CI, 0.54-0.62). Marginal association was found for lung cancer risk (OR = 0.87, 95% CI, 0.81-0.92). The linear trend between increased parity and reduced cancer risk was dramatically more pronounced for breast and ovarian cancer than the other cancers included in the analysis. This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies.

19 Testing causality in the association of plasma cortisol with risk of coronary heart disease: a mendelian randomisation study

Elevated morning plasma cortisol is associated with multiple cardiovascular risk factors in metabolic syndrome. Epidemiological studies have reported positive associations between plasma cortisol and coronary heart disease (CHD) however traditional observational studies do not allow causality to be determined and are unable rule out the possibility of confounding. A two-sample Mendelian randomisation approach was used to estimate the causal effect of plasma cortisol on risk of CHD. A genetic instrument for plasma cortisol comprised three SNPs which were associated with plasma cortisol in the recent Cortisol Network (CORNET) genome wide association meta-analysis (GWAMA) (n = 12,597). We investigated the association between this genetic instrument and risk of CHD in 22,223 cases/64,762 controls from the CARDIOGRAM consortium. Each standard deviation rise in genetically predicted plasma cortisol was associated with an odds ratio of 1.27 (95% CI: 1.01–1.60) for CHD. These results are compatible with a causal effect for the observational association between plasma cortisol and CHD. The inconsistent results from observational studies may be explained by: the inverse association between cortisol and obesity, which confounded the positive association of cortisol with other cardiovascular risk factors; and the use of single ‘snapshot’ plasma cortisol measurement rather than cumulative measure of cortisol exposure provided by genetic prediction. A bidirectional Mendelian randomisation analysis between plasma cortisol and BMI may yield greater clarity. To improve the strength of our genetic instrument an expanded CORNET GWAMA is currently underway. Measurements of cortisol may add value to predictions of CHD risk.

Psychoactive medicine use and the risk of hip fracture in older people: a case-crossover study.

Many factors can increase the risk of hip fracture, including medicines. Traditional observational studies have assessed the risk, but results may be confounded by unmeasured factors. The case-crossover design is an alternative approach that controls for patient-specific, time-invariant confounding factors. This study aimed to assess associations between psychoactive medicines and hip fracture in the elderly using the case-crossover method. Data were sourced from the Australian Government Department of Veterans' Affairs healthcare claims database. The study population comprised beneficiaries aged over 65 years who were hospitalised for hip fracture between 2009 and 2012. The case-crossover method was used to compare individuals' medicine exposure immediately before hip fracture (case window) with their exposure at an earlier time (control window). Conditional logistic regression was employed to quantify associations between medicine use and hip fracture. Alternative exposure windows were assessed in sensitivity analyses. Eight thousand eight hundred twenty-eight patients were hospitalised for hip fracture. Their median age was 88 years, and 63% were female. Odds of hip fracture were increased for opioids (odds ratio [OR] = 1.62, 95% confidence interval [CI] = 1.42-1.84), selective serotonin reuptake inhibitors (OR = 1.54, 95% CI = 1.28-1.86) and antipsychotics (OR = 1.47, 95% CI = 1.21-1.80). There was no significant association for tricyclic antidepressants in the primary analysis (OR = 1.18, 95% CI = 0.91-1.52), but there was a significant association in the sensitivity analysis using an alternative, earlier control window (OR = 1.43, 95% CI = 1.11-1.83). Increased risk of hip fracture in older people was found for opioids, selective serotonin reuptake inhibitors and antipsychotics. The choice of control window influenced the result for tricyclic antidepressants.