2,909 research outputs found

    Inter-individual variation of the human epigenome & applications

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    Understanding and improving the applicability of randomised controlled trials: subgroup reporting and the statistical calibration of trials to real-world populations

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    Context and objective Randomised controlled trials (hereafter, trials) are widely regarded as the gold standard for evaluating treatment efficacy in medical interventions. They employ strict study designs, rigorous eligibility criteria, standardised protocols, and close participant monitoring under controlled conditions, contributing to high internal validity. However, these stringent criteria and procedures may limit the generalisability of trial findings to real-world situations, which often involve diverse patient populations such as multimorbidity and frailty patients. Consequently, there is growing interest in the applicability of trials to real-world clinical practice. In this thesis I will 1) evaluate how well major trials report on variation in treatment effects and 2) examine the use of trial calibration methods to test trial applicability. Methods 1) A comprehensive and consistent subgroup reporting description was presented, which contributes to the exploration of subgroup effects and treatment heterogeneity for informed decision-making in tailored subgroup populations within routine practice. The study evaluated 2,235 trials from clinicaltrial.gov that involve multiple chronic medical conditions, assessing the presence of subgroup reporting in corresponding publications and extracting subgroup terms. These terms were then standardised and summarised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Logistic and Poisson regression models were employed to identify independent predictors of subgroup reporting patterns. 2) Two calibration models, namely the regression-based model and inverse odds of sampling weights (IOSW) were implemented. These models were utilised to apply the findings from two influential heart failure (HF) trials - COMET and DIG - to a real-world HF registry in Scotland consisting of 8,012 HF patients mainly with reduced ejection fraction, using individual participant data (IPD) from both datasets. Additionally, calibration was conducted within the subgroup population (lowest and highest risk group) of the real-world Scottish HF registry for exploratory analyses. The study provided comparisons of baseline characteristics and calibrated and uncalibrated results between the trial and registry. Furthermore, it assessed the impact of calibration on the results with the focus on overall effects and precision. Results The subgroup reporting study showed that among 2,235 eligible trials, 48% (1,082 trials) reported overall results and 23% (524 trials) reported subgroups. Age (51%), gender (45%), racial group (28%) and geographical locations (17%) were the most frequently reported subgroups among 524 trials. Characteristics related to the index condition (severity/duration/types, etc.) were somewhat commonly reported. However, reporting on metrics of comorbidity or frailty and mental health were rare. Follow-up time, enrolment size, trial starting year and specific index conditions (e.g., hypercholesterolemia, hypertension etc.) were significant predictors for any subgroup reporting after adjusting for enrolment size and index conditions while funding source and number of arms were not associated with subgroup reporting. The trial calibration study showed that registry patients were, on average, older, had poorer renal function and received higher-doses of loop diuretics than trial participants. The key findings from two HF trials remained consistent after calibration in the registry, with a tolerable decrease in precision (larger confidence intervals) for the effect estimates. Treatment-effect estimates were also similar when trials were calibrated to high-risk and low-risk registry patients, albeit with a greater reduction in precision. Conclusion Variations in subgroup reporting among different trials limited the feasibility to evaluate subgroup effects and examine heterogeneity of treatment effects. If IPD or IPD alternative summarised data is available from trials and the registry, trial applicability can be assessed by performing calibration

    Determinants of embryonic and foetal growth

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    The main aims of this thesis were:1. To investigate whether there are associations between determinants related to the living environment (in particular neighbourhood deprivation and air pollution) and embryonic growth, foetal growth and pregnancy outcomes;2. To assess the associations between maternal cardiometabolic determinants in pregnancy (lipid status and the presence of hypertensive disorders of pregnancy)and embryonic growth, foetal growth and childhood outcomes;3. To investigate the impact of neighbourhood deprivation on the effectiveness ofthe mHealth “Smarter Pregnancy” program, aimed at improving nutrition and lifestyle behaviours;<br/

    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    Determinants of embryonic and foetal growth

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    The main aims of this thesis were:1. To investigate whether there are associations between determinants related to the living environment (in particular neighbourhood deprivation and air pollution) and embryonic growth, foetal growth and pregnancy outcomes;2. To assess the associations between maternal cardiometabolic determinants in pregnancy (lipid status and the presence of hypertensive disorders of pregnancy)and embryonic growth, foetal growth and childhood outcomes;3. To investigate the impact of neighbourhood deprivation on the effectiveness ofthe mHealth “Smarter Pregnancy” program, aimed at improving nutrition and lifestyle behaviours;<br/

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Inter-individual variation of the human epigenome &amp; applications

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    Genome-wide association studies (GWAS) have led to the discovery of genetic variants influencing human phenotypes in health and disease. However, almost two decades later, most human traits can still not be accurately predicted from common genetic variants. Moreover, genetic variants discovered via GWAS mostly map to the non-coding genome and have historically resisted interpretation via mechanistic models. Alternatively, the epigenome lies in the cross-roads between genetics and the environment. Thus, there is great excitement towards the mapping of epigenetic inter-individual variation since its study may link environmental factors to human traits that remain unexplained by genetic variants. For instance, the environmental component of the epigenome may serve as a source of biomarkers for accurate, robust and interpretable phenotypic prediction on low-heritability traits that cannot be attained by classical genetic-based models. Additionally, its research may provide mechanisms of action for genetic associations at non-coding regions that mediate their effect via the epigenome. The aim of this thesis was to explore epigenetic inter-individual variation and to mitigate some of the methodological limitations faced towards its future valorisation.Chapter 1 is dedicated to the scope and aims of the thesis. It begins by describing historical milestones and basic concepts in human genetics, statistical genetics, the heritability problem and polygenic risk scores. It then moves towards epigenetics, covering the several dimensions it encompasses. It subsequently focuses on DNA methylation with topics like mitotic stability, epigenetic reprogramming, X-inactivation or imprinting. This is followed by concepts from epigenetic epidemiology such as epigenome-wide association studies (EWAS), epigenetic clocks, Mendelian randomization, methylation risk scores and methylation quantitative trait loci (mQTL). The chapter ends by introducing the aims of the thesis.Chapter 2 focuses on stochastic epigenetic inter-individual variation resulting from processes occurring post-twinning, during embryonic development and early life. Specifically, it describes the discovery and characterisation of hundreds of variably methylated CpGs in the blood of healthy adolescent monozygotic (MZ) twins showing equivalent variation among co-twins and unrelated individuals (evCpGs) that could not be explained only by measurement error on the DNA methylation microarray. DNA methylation levels at evCpGs were shown to be stable short-term but susceptible to aging and epigenetic drift in the long-term. The identified sites were significantly enriched at the clustered protocadherin loci, known for stochastic methylation in neurons in the context of embryonic neurodevelopment. Critically, evCpGs were capable of clustering technical and longitudinal replicates while differentiating young MZ twins. Thus, discovered evCpGs can be considered as a first prototype towards universal epigenetic fingerprint, relevant in the discrimination of MZ twins for forensic purposes, currently impossible with standard DNA profiling. Besides, DNA methylation microarrays are the preferred technology for EWAS and mQTL mapping studies. However, their probe design inherently assumes that the assayed genomic DNA is identical to the reference genome, leading to genetic artifacts whenever this assumption is not fulfilled. Building upon the previous experience analysing microarray data, Chapter 3 covers the development and benchmarking of UMtools, an R-package for the quantification and qualification of genetic artifacts on DNA methylation microarrays based on the unprocessed fluorescence intensity signals. These tools were used to assemble an atlas on genetic artifacts encountered on DNA methylation microarrays, including interactions between artifacts or with X-inactivation, imprinting and tissue-specific regulation. Additionally, to distinguish artifacts from genuine epigenetic variation, a co-methylation-based approach was proposed. Overall, this study revealed that genetic artifacts continue to filter through into the reported literature since current methodologies to address them have overlooked this challenge.Furthermore, EWAS, mQTL and allele-specific methylation (ASM) mapping studies have all been employed to map epigenetic variation but require matching phenotypic/genotypic data and can only map specific components of epigenetic inter-individual variation. Inspired by the previously proposed co-methylation strategy, Chapter 4 describes a novel method to simultaneously map inter-haplotype, inter-cell and inter-individual variation without these requirements. Specifically, binomial likelihood function-based bootstrap hypothesis test for co-methylation within reads (Binokulars) is a randomization test that can identify jointly regulated CpGs (JRCs) from pooled whole genome bisulfite sequencing (WGBS) data by solely relying on joint DNA methylation information available in reads spanning multiple CpGs. Binokulars was tested on pooled WGBS data in whole blood, sperm and combined, and benchmarked against EWAS and ASM. Our comparisons revealed that Binokulars can integrate a wide range of epigenetic phenomena under the same umbrella since it simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing or even unknown epigenetic processes. Finally, we verified examples of mQTL and polymorphic imprinting by employing another novel tool, JRC_sorter, to classify regions based on epigenotype models and non-pooled WGBS data in cord blood. In the future, we envision how this cost-effective approach can be applied on larger pools to simultaneously highlight regions of interest in the methylome, a highly relevant task in the light of the post-GWAS era.Moving towards future applications of epigenetic inter-individual variation, Chapters 5 and 6 are dedicated to solving some of methodological issues faced in translational epigenomics.Firstly, due to its simplicity and well-known properties, linear regression is the starting point methodology when performing prediction of a continuous outcome given a set of predictors. However, linear regression is incompatible with missing data, a common phenomenon and a huge threat to the integrity of data analysis in empirical sciences, including (epi)genomics. Chapter 5 describes the development of combinatorial linear models (cmb-lm), an imputation-free, CPU/RAM-efficient and privacy-preserving statistical method for linear regression prediction on datasets with missing values. Cmb-lm provide prediction errors that take into account the pattern of missing values in the incomplete data, even at extreme missingness. As a proof-of-concept, we tested cmb-lm in the context of epigenetic ageing clocks, one of the most popular applications of epigenetic inter-individual variation. Overall, cmb-lm offer a simple and flexible methodology with a wide range of applications that can provide a smooth transition towards the valorisation of linear models in the real world, where missing data is almost inevitable. Beyond microarrays, due to its high accuracy, reliability and sample multiplexing capabilities, massively parallel sequencing (MPS) is currently the preferred methodology of choice to translate prediction models for traits of interests into practice. At the same time, tobacco smoking is a frequent habit sustained by more than 1.3 billion people in 2020 and a leading (and preventable) health risk factor in the modern world. Predicting smoking habits from a persistent biomarker, such as DNA methylation, is not only relevant to account for self-reporting bias in public health and personalized medicine studies, but may also allow broadening forensic DNA phenotyping. Previously, a model to predict whether someone is a current, former, or never smoker had been published based on solely 13 CpGs from the hundreds of thousands included in the DNA methylation microarray. However, a matching lab tool with lower marker throughput, and higher accuracy and sensitivity was missing towards translating the model in practice. Chapter 6 describes the development of an MPS assay and data analysis pipeline to quantify DNA methylation on these 13 smoking-associated biomarkers for the prediction of smoking status. Though our systematic evaluation on DNA standards of known methylation levels revealed marker-specific amplification bias, our novel tool was still able to provide highly accurate and reproducible DNA methylation quantification and smoking habit prediction. Overall, our MPS assay allows the technological transfer of DNA methylation microarray findings and models to practical settings, one step closer towards future applications.Finally, Chapter 7 provides a general discussion on the results and topics discussed across Chapters 2-6. It begins by summarizing the main findings across the thesis, including proposals for follow-up studies. It then covers technical limitations pertaining bisulfite conversion and DNA methylation microarrays, but also more general considerations such as restricted data access. This chapter ends by covering the outlook of this PhD thesis, including topics such as bisulfite-free methods, third-generation sequencing, single-cell methylomics, multi-omics and systems biology.<br/

    Keep on track:Monitoring growth and development in children born preterm and full-term

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    Environmental factors in early life influence early development and growth, and influence long-term health. In this thesis we showed that in premature infants, the length and growth of the cerebral cortex (corpus callosum) is a good marker of brain growth and a predictor of later neurological development. An eye-tracking test (watching a video) at the age of 1 year also appeared to be a predictive factor for overall cognitive and motor development 1 year later. We also found that weight gain after preterm birth is associated with body composition in childhood. We also compared 2 methods of measuring body composition in children (DXA and ADP) and found that the results of fat mass (percentage) and fat-free mass at 3-5 years of age differ significantly between both methods, and that these differences are greater in very preterm children compared to full-term children. We present improvements to the algorithm to improve results with ADP. We also investigated sleep and found that parent-reported sleep characteristics and problems are similar between very preterm and full-term children at the age of 3 years. In the general population, we have shown that low birth weight (&lt;2500 grams) and growth retardation during fetal life and childhood are associated with longer sleep duration and higher sleep efficiency at 10-15 years of age. Furthermore, at the same school age, greater intraday variability (fragmentation of the 24-hour activity rhythm) was associated with a higher fat mass index and higher visceral fat mass in boys

    A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

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    Early detection of diabetes is crucial because of its incurable nature. Several diabetes prediction models have been developed using machine learning techniques (MLTs). The performance of MLTs varies for different accuracy measures. Thus, selecting appropriate MLTs for diabetes prediction is challenging. This paper proposes a multi-criteria decision-making (MCDM) based framework for evaluating MLTs applied to diabetes prediction. Initially, three MCDM methods—WSM, TOPSIS, and VIKOR—are used to determine the individual ranks of MLTs for diabetes prediction performance by using various comparable performance measures (PMs). Next, a fusion approach is used to determine the final rank of the MLTs. The proposed method is validated by assessing the performance of 10 MLTs on the Pima Indian diabetes dataset using eight evaluation metrics for diabetes prediction. Based on the final MCDM rankings, logistic regression is recommended for diabetes prediction modeling
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