scholarly journals 1057Addressing challenges in life-course epidemiology: established and novel approaches using big data and twin/family studies

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Miriam Mosing ◽  
Bronwyn Brew ◽  
Alison Gibberd ◽  
Malin Ericsson ◽  
Kelli Lehto ◽  
...  

Abstract Focus and outcomes for participants Long periods between exposures and outcomes pose a number of challenges for life course epidemiological research, including unmeasured confounding factors (e.g.; familial factors) and mediation by other covariates, which make it difficult to unequivocally establish associations let alone causality. In this symposium we will present a number of different studies based on big data utilizing a variety of methods to overcome some of the issues encountered in research across long time frames or generations. Our focus will be on the different methods, the solutions they provide as well as their limitations. The methods presented were applied in the context of life course epidemiology and include: mediation analyses; genetic epidemiology; well-established and novel family designs including twins, siblings and cousins, and a method comparable to Mendelian randomization - ICE FALCON (Inference on Causation from Examination of Familial Confounding) which is part of a more general approach called ICE CRiSTAL (Inference on Causation from Examining Changes in Regression coefficients in STatistical AnaLsyes). The intended outcomes for the audience are to increase awareness of the challenges imposed by the data frequently used in this field of research and present possible solutions to (at least partly) address those. It is our intention to generate discussion and encourage other researchers to share their experiences and solutions to increase knowledge collectively. Rationale for the symposium, including for its inclusion in the Congress The main theme of the conference is ‘Methodological Innovations in Epidemiology’. Our symposium includes six different methods to strengthen causal inferences in epidemiology. While some of the presented methods are well established in classic epidemiology research (i.e. mediation analyses), others are more commonly found in different disciplines such as the expanding genetic epidemiology field (e.g. alternative twin designs and measured genetic risk approaches). In addition, we are presenting a new method for making inference about causation developed by Prof. John Hopper and Dr Shuai Li and co-workers called ICE FALCON, which applies to twin and family data and is part of a more general approach called ICE CRiSTAL. These methods use observational data to infer or rebut causality between measured variables, similar to Mendelian randomization (without relying on genetic information or strong assumptions). All the work presented is either nearing publication or has been published in the last two years and each presenter is intimately involved with the analysis and methods they will be presenting. Beyond a range of methods and study designs we have also a diversity of researchers and research questions in our symposium by including: researchers at different stages in their career and from around the world (ranging from early Postdoctoral Fellows over Senior Research Fellows/Assistant professors to Professors); a variety of research questions and diseases; and a range of population context including Indigenous Australians, babies, children, and adults, in order to appeal to a wider audience. Presentation program 6 talks of 8 minutes each with 2 minutes for questions followed by a general discussion facilitated by the chair. Names of presenters Dr Miriam A Mosing1,2

2021 ◽  
pp. 1-10
Author(s):  
Xian Li ◽  
Yan Tian ◽  
Yu-Xiang Yang ◽  
Ya-Hui Ma ◽  
Xue-Ning Shen ◽  
...  

Background: Several studies showed that life course adiposity was associated with Alzheimer’s disease (AD). However, the underlying causality remains unclear. Objective: We aimed to examine the causal relationship between life course adiposity and AD using Mendelian randomization (MR) analysis. Methods: Instrumental variants were obtained from large genome-wide association studies (GWAS) for life course adiposity, including birth weight (BW), childhood body mass index (BMI), adult BMI, waist circumference (WC), waist-to-hip ratio (WHR), and body fat percentage (BFP). A meta-analysis of GWAS for AD including 71,880 cases and 383,378 controls was used in this study. MR analyses were performed using inverse variance weighted (IVW), weighted median, and MR-Egger regression methods. We calculated odds ratios (ORs) per genetically predicted standard deviation (1-SD) unit increase in each trait for AD. Results: Genetically predicted 1-SD increase in adult BMI was significantly associated with higher risk of AD (IVW: OR = 1.03, 95% confidence interval [CI] = 1.01–1.05, p = 2.7×10–3) after Bonferroni correction. The weighted median method indicated a significant association between BW and AD (OR = 0.94, 95% CI = 0.90–0.98, p = 1.8×10–3). We also found suggestive associations of AD with WC (IVW: OR = 1.03, 95% CI = 1.00–1.07, p = 0.048) and WHR (weighted median: OR = 1.04, 95% CI = 1.00–1.07, p = 0.029). No association was detected of AD with childhood BMI and BFP. Conclusion: Our study demonstrated that lower BW and higher adult BMI had causal effects on increased AD risk.


2018 ◽  
Vol 64 (2) ◽  
pp. 374-385 ◽  
Author(s):  
Ingrid W Moen ◽  
Helle K M Bergholdt ◽  
Thomas Mandrup-Poulsen ◽  
Børge G Nordestgaard ◽  
Christina Ellervik

Abstract BACKGROUND It is unknown why increased plasma ferritin concentration predicts all-cause mortality. As low-grade inflammation and increased plasma ferritin concentration are associated with all-cause mortality, we hypothesized that increased plasma ferritin concentration is genetically associated with low-grade inflammation. METHODS We investigated whether increased plasma ferritin concentration is associated with low-grade inflammation [i.e., increased concentrations of C-reactive protein (CRP) and complement component 3 (C3)] in 62537 individuals from the Danish general population. We also applied a Mendelian randomization approach, using the hemochromatosis genotype C282Y/C282Y as an instrument for increased plasma ferritin concentration, to assess causality. RESULTS For a doubling in plasma ferritin concentration, the odds ratio (95% CI) for CRP ≥2 vs <2 mg/L was 1.12 (1.09–1.16), with a corresponding genetic estimate for C282Y/C282Y of 1.03 (1.01–1.06). For a doubling in plasma ferritin concentration, odds ratio (95% CI) for complement C3 >1.04 vs ≤1.04 g/L was 1.28 (1.21–1.35), and the corresponding genetic estimate for C282Y/C282Y was 1.06 (1.03–1.12). Mediation analyses showed that 74% (95% CI, 24–123) of the association of C282Y/C282Y with risk of increased CRP and 56% (17%–96%) of the association of C282Y/C282Y with risk of increased complement C3 were mediated through plasma ferritin concentration. CONCLUSIONS Increased plasma ferritin concentration as a marker of increased iron concentration is associated observationally and genetically with low-grade inflammation, possibly indicating a causal relationship from increased ferritin to inflammation. However, as HFE may also play an immunological role indicating pleiotropy and as incomplete penetrance of C282Y/C282Y indicates buffering mechanisms, these weaknesses in the study design could bias the genetic estimates.


2017 ◽  
Author(s):  
Lavinia Paternoster ◽  
Kate Tilling ◽  
George Davey Smith

The past decade has been proclaimed as a hugely successful era of gene discovery through the high yields of many genome-wide association studies (GWAS). However, much of the perceived benefit of such discoveries lies in the promise that the identification of genes that influence disease would directly translate into the identification of potential therapeutic targets (1-4), but this has yet to be realised at a level reflecting expectation. One reason for this, we suggest, is that GWAS to date have generally not focused on phenotypes that directly relate to the progression of disease, and thus speak to disease treatment.


Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.


Author(s):  
Ali Yazici ◽  
Ziya Karakaya ◽  
Mohammed Alayyoub

The choice of the most effective stream processing framework (SPF) for Big Data has been an important issue among the researchers and practioners. Each of the SPFs has different cutting edge technologies in their steps of processing the data in motion that gives them a better advantage over the others. Even though, these technologies used in each SPF might better them, it is still difficult to conclude which framework berforms better under different scenarios and conditions. In this paper, we aim to show trends and differences about several SPFs for Big Data by using the so called Systematic Mapping (SM) approach using the related research outcomes. To achieve this objective, nine research questions (RQs) were raised, in which 91 studies that were conducted between 2010 and 2015 were evaluated. We present the trends by classifying the research on SPFs with respect to the proposed RQs which can direct researchers in getting an state-of-art overview of the field.


2018 ◽  
Vol 10 (3) ◽  
pp. 299-305 ◽  
Author(s):  
S. Santos ◽  
D. Zugna ◽  
C. Pizzi ◽  
L. Richiardi

AbstractIn epidemiologic analytical studies, the primary goal is to obtain a valid and precise estimate of the effect of the exposure of interest on a given outcome in the population under study. A crucial source of violation of the internal validity of a study involves bias arising from confounding, which is always a challenge in observational research, including life course epidemiology. The increasingly popular approach of meta-analyzing individual participant data from several observational studies also brings up to discussion the problem of confounding when combining data from different populations. In this study, we review and discuss the most common sources of confounding in life course epidemiology: (i) confounding by indication, (ii) impact of baseline selection on confounding, (iii) time-varying confounding and (iv) mediator–outcome confounding. We also discuss the issue of addressing confounding in the context of an individual participant data meta-analysis.


Author(s):  
Gita D. Mishra ◽  
Diana Kuh ◽  
Yoav Ben-Shlomo

2018 ◽  
Vol 72 (6) ◽  
pp. 507-512
Author(s):  
Anne Gosselin ◽  
Annabel Desgrées du Loû ◽  
Eva Lelièvre

BackgroundLife course epidemiology is now an established field in social epidemiology; in sociodemography, the quantitative analysis of biographies recently experienced significant trend from event history analysis to sequence analysis. The purpose of this article is to introduce and adapt this methodology to a social epidemiology question, taking the example of the impact of HIV diagnosis on Sub-Saharan migrants’ residential trajectories in the Paris region.MethodsThe sample consists of 640 migrants born in Sub-Saharan Africa receiving HIV care. They were interviewed in healthcare facilities in the Paris region within the PARCOURS project, conducted from 2012 to 2013, using life event history calendars, which recorded year by year their health, family and residential histories. We introduce a two-step methodological approach consisting of (1) sequence analysis by optimal matching to build a typology of migrants’ residential pathways before and after diagnosis, and (2) a Cox model of the probability to experience changes in the residential situation.ResultsThe seven-clusters typology shows that for a majority, the HIV diagnosis did not entail changes in residential situation. However 30% of the migrants experienced a change in their residential situation at time of diagnosis. The Cox model analysis reveals that this residential change was in fact moving in with one’s partner (HR 2.99, P<0.000) rather than network rejection.ConclusionThis original combination of sequence analysis and Cox models is a powerful process that could be applied to other themes and constitutes a new approach in the life course epidemiology toolbox.Trial registration numberNCT02566148.


Author(s):  
Kağan Okatan

All these types of analytics have been answering business questions for a long time about the principal methods of investigating data warehouses. Especially data mining and business intelligence systems support decision makers to reach the information they want. Many existing systems are trying to keep up with a phenomenon that has changed the rules of the game in recent years. This is undoubtedly the undeniable attraction of 'big data'. In particular, the issue of evaluating the big data generated especially by social media is among the most up-to-date issues of business analytics, and this issue demonstrates the importance of integrating machine learning into business analytics. This section introduces the prominent machine learning algorithms that are increasingly used for business analytics and emphasizes their application areas.


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