Biology subjects

Martin, R. M.

Publications and source records attributed to Martin, R. M..

9 recordsLinked to original sources

bioRxiv2018

Mendelian randomization does not support serum calcium in prostate cancer risk

Background: Observational studies suggest that dietary and serum calcium are risk factors for prostate cancer. However, such studies suffer from residual confounding (due to unmeasured or imprecisely measured confounders), undermining causal inference. Mendelian randomization uses randomly assigned (hence unconfounded and pre-disease onset) germline genetic variation to proxy for phenotypes and strengthen causal inference in observational studies.\n\nObjective: We tested the hypothesis that serum calcium is associated with an increased risk of overall and advanced prostate cancer.\n\nDesign: A genetic instrument was constructed using 5 single nucleotide polymorphisms robustly associated with serum calcium in a genome-wide association study (N [≤] 61,079). This instrument was then used to test the effect of a 0.5 mg/dL increase (1 standard deviation, SD) in serum calcium on risk of prostate cancer in 72,729 men in the PRACTICAL (Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome) Consortium (44,825 cases, 27,904 controls) and risk of advanced prostate cancer in 33,498 men (6,263 cases, 27,235 controls).\n\nResults: We found weak evidence for a protective effect of serum calcium on prostate cancer risk (odds ratio [OR] per 0.5 mg/dL increase in calcium: 0.83, 95% CI: 0.63-1.08; P=0.12). We did not find strong evidence for an effect of serum calcium on advanced prostate cancer (OR per 0.5 mg/dL increase in calcium: 0.98, 95% CI: 0.57-1.70; P=0.93).\n\nConclusions: Our Mendelian randomization analysis does not support the hypothesis that serum calcium increases risk of overall or advanced prostate cancer.

epidemiology↗

bioRxiv2017

Causal inference in cancer epidemiology: what is the role of Mendelian randomization?

Observational epidemiological studies are prone to confounding, measurement error, and reverse causation, undermining their ability to generate reliable causal estimates of the effect of risk factors to inform cancer prevention and treatment strategies. Mendelian randomization (MR) is an analytical approach that uses genetic variants to proxy potentially modifiable exposures (e.g. environmental factors, biological traits, and druggable pathways) to permit robust causal inference of the effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within population health research in cardio-metabolic disease, but also holds much promise for identifying possible interventions (e.g., dietary, behavioural, or pharmacological) for cancer prevention and treatment. However, some methodological and conceptual challenges in the implementation of MR are particularly pertinent when applying this method to cancer aetiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies.\n\nIn this review, we provide an overview of the key principles and assumptions of MR focusing on applications of this method to the study of cancer aetiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared to conventional epidemiological studies. Lastly, limitations of MR and recent methodological developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer.

epidemiology↗

bioRxiv2017

Circulating selenium and prostate cancer risk: a Mendelian randomization analysis

In the Selenium and Vitamin E Cancer Prevention Trial (SELECT), selenium supplementation (causing a median 114 g/L increase in circulating selenium) did not lower overall prostate cancer risk, but increased risk of high-grade prostate cancer and type 2 diabetes. Mendelian randomization analysis uses genetic variants to proxy modifiable risk factors and can strengthen causal inference in observational studies. We constructed a genetic risk score comprising eleven single-nucleotide polymorphisms robustly (P<5x10-8) associated with circulating selenium in genome-wide association studies. In a Mendelian randomization analysis of 72,729 men in the PRACTICAL Consortium (44,825 cases, 27,904 controls), 114 g/L higher genetically-elevated circulating selenium was not associated with prostate cancer (OR: 1.01; 95% CI: 0.89-1.13). Concordant with findings from SELECT, selenium was weakly associated with advanced (including high-grade) prostate cancer (OR: 1.21; 95% CI: 0.98-1.49) and type 2 diabetes (OR: 1.18; 95% CI: 0.97-1.43; in a type 2 diabetes GWAS meta-analysis with up to 49,266 cases, 249,906 controls). Mendelian randomization mirrored the outcome of selenium supplementation in SELECT and may offer an approach for the prioritization of interventions for follow-up in large-scale randomized controlled trials.

epidemiology↗

bioRxiv2017

Single-molecule FISH in Drosophila muscle reveals location dependent mRNA composition of megaRNPs

Single-molecule fluorescence in-situ hybridization (smFISH) provides direct access to the spatial relationship between nucleic acids and specific subcellular locations. The ability to precisely localize a messenger RNA can reveal key information about its regulation. Although smFISH is well established in cell culture or thin sections, methods for its accurate application to tissues are lacking. The utility of smFISH in thick tissue sections must overcome several challenges, including probe penetration of fixed tissue, accessibility of target mRNAs for probe hybridization, high fluorescent background, spherical aberration along the optical axis, and image segmentation of organelles. Here we describe how we overcame these obstacles to study mRNA localization in Drosophila larval muscle samples that approach 50 m thickness. We use sample-specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3-dimensional multiple-organelle segmentation. The latter allows using independent thresholds for different regions of interest within an image stack. Our approach therefore facilitates accurate measurement of mRNA location in thick tissues.

cell biology↗

bioRxiv2017

Associations Of Prenatal And Postnatal Growth With Insulin-Like Growth Factor-I Levels In Pre-Adolescence

BackgroundRapid pre - and postnatal growth have been associated with later life adverse health outcomes, which could implicate (as a mediator) circulating insulin-like-growth-factor I (IGF-I), an important regulator of growth. We investigated associations of prenatal (birth weight and length) and postnatal growth in infancy and childhood with circulating IGF-I measured at 11.5 years of age.\n\nMethodsWe analysed 11.5-year follow-up data from 17,046 Belarusian children who participated in the Promotion of Breastfeeding Intervention Trial (PROBIT) since birth.\n\nResultsComplete data were available for 5422 boys and 4743 girls (60%). We stratified the analyses by sex, as there was evidence of interaction between growth and sex in their associations with IGF-I. Weight and length/height velocity during childhood were positively associated with IGF-I at 11.5 years; associations increased with age at growth assessment and were stronger for length/height gain than for weight gain. The change in internal run-normalized IGF-I z-score at 11.5 years was 0.038 (95% CI -0.004,0.080) per standard deviation (SD) increase in length gain at 0-3 months amongst girls and 0.025 (95% CI - 0.011,0.060) amongst boys, increasing to 0.336 (95% CI 0.281,0.391;) and 0.211 (95% CI 0.165,0.256) for girls and boys, respectively, for growth during 6.5-11.5 years.\n\nConclusionPostnatal growth velocities in childhood are positively associated with levels of circulating IGF-I in pre-adolescents. Future studies should focus on assessing whether IGF-I is on the causal pathway between early growth and later health outcomes, such as cancer and diabetes.

epidemiology↗

bioRxiv2017

MELODI - Mining Enriched Literature Objects to Derive Intermediates

MotivationThe scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, prioritising mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritise mechanisms for more focused and detailed analysis.\n\nResultsHere we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts. Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. Firstly, an analysis of ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Secondly, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro.\n\nAvailabilityMELODI has been implemented as a Python/Django web application, and is freely available to use at www.melodi.biocompute.org.uk\n\nContactmelodi@biocompute.org.uk

bioinformatics↗

bioRxiv2017

Mendelian randomization: a novel approach for the prediction of adverse drug events and drugrepurposing opportunities

Identification of unintended drug effects, specifically drug repurposing opportunities and adverse drug events, maximizes the benefit of a drug and protects the health of patients. However, current observational research methods are subject to several biases. These include confounding by indication, reverse causality, and missing data. We propose that Mendelian randomization (MR) offers a novel approach for the prediction of unintended drug effects. In particular, we advocate the synthesis of evidence from this method and other approaches, in the spirit of triangulation, to improve causal inferences concerning drug effects. MR overcomes some of the limitations associated with the existing methods in this field. Furthermore, it can be applied either pre- or post-approval of the drug and could therefore prevent the potentially harmful exposure of patients in clinical trials and beyond. The potential of MR as a pharmacovigilance and drug repurposing tool is yet to be realized and could both help prevent adverse drug events and identify novel indications for existing drugs in the future.

pharmacology and toxicology↗

bioRxiv2016

MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations

Published genetic associations can be used to infer causal relationships between phenotypes, bypassing the need for individual-level genotype or phenotype data. We have curated complete summary data from 1094 genome-wide association studies (GWAS) on diseases and other complex traits into a centralised database, and developed an analytical platform that uses these data to perform Mendelian randomization (MR) tests and sensitivity analyses (MR-Base, http://www.mrbase.org). Combined with curated data of published GWAS hits for phenomic measures, the MR-Base platform enables millions of potential causal relationships to be evaluated. We use the platform to predict the impact of lipid lowering on human health. While our analysis provides evidence that reducing LDL-cholesterol, lipoprotein(a) or triglyceride levels reduce coronary disease risk, it also suggests causal effects on a number of other non-vascular outcomes, indicating potential for adverse-effects or drug repositioning of lipid-lowering therapies.

epidemiology↗

bioRxiv2016

Power calculator for instrumental variable analysis in pharmacoepidemiology

BackgroundInstrumental variable analysis, for example with physicians prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomisation power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research.\n\nMethods and resultsThe formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. A web application is provided for the implementation of the formula by others.\n\nConclusionsThe statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power.\n\nFUNDING STATEMENTThis work was supported by the Perros Trust and the Integrative Epidemiology Unit. The Integrative Epidemiology Unit is supported by the Medical Research Council and the University of Bristol [grant number MC_UU_12013/9]. Stephen Burgess is supported by a post-doctoral fellowship from the Wellcome Trust [100114].\n\nKey MessagesO_LIResearch questions using instrumental variable analysis in pharmacoepidemiology have distinct structures that have previously not been catered for by instrumental variable analysis power calculators.\nC_LIO_LIPower can be calculated for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome in the context of pharmacoepidemiology using the presented formula and online power calculator.\nC_LIO_LIThe use of this power calculator will allow investigators to determine whether a pharmacoepidemiology study is likely to detect clinically meaningful treatment effects prior to the studys commencement.\nC_LI

epidemiology↗