Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2 alleles

Martin, Richard M; Vaher, Kadi; Bermingham, Mairead Lesley; Morris, Stewart W.; Bretherick, Andrew D.; Zeng, Yanni; Rawlik, Konrad; Amador, Carmen; Campbell, Archie; Haley, Chris; Hayward, Caroline; Porteous, David J.; McIntosh, Andrew M.; Marioni, Riccardo E.; Evans, Kathryn

doi:10.1186/s13073-020-00808-4

Cited by 61 publications

(43 citation statements)

References 75 publications

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“…This result indicates combining multiple omics data can yield better insights into the underlying mechanisms of diseases. It can also be observed that the classification performance using DNA methylation profile alone and miRNA expression profile alone was almost identical and the performance of the two different multi-omics combinations (i.e., gene expression+DNA mehyltaion and gene expression+DNA methylation+miRNA expression) was not significantly different, which indicated that miRNA expression profiles and DNA methylation profiles providing overlapping information for the tumour type classification task [ 37 , 38 ].…”

Section: Resultsmentioning

confidence: 99%

OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

Zhang

Xing

Sun

et al. 2021

Cancers

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High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called “the curse of dimensionality” in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.

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Section: Resultsmentioning

confidence: 99%

OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

Zhang

Xing

Sun

et al. 2021

Cancers

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“…Human data on postbiotics and DM are lacking, and we identified only one trial on this topic in healthy adults [ 85 ]. It is likely that ethnic group [ 138 , 139 ], sex [ 140 ], and the time course of DM [ 141 ] influence the GM response to postbiotic treatments, and these aspects should be contemplated in human studies. Many of the included studies did not analyse the changes in the GM following the interventions, and this would provide valuable information about the underlying mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Role of Postbiotics in Diabetes Mellitus: Current Knowledge and Future Perspectives

Cabello-Olmo

Araña

Urtasun

et al. 2021

Foods

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In the last decade, the gastrointestinal microbiota has been recognised as being essential for health. Indeed, several publications have documented the suitability of probiotics, prebiotics, and symbiotics in the management of different diseases such as diabetes mellitus (DM). Advances in laboratory techniques have allowed the identification and characterisation of new biologically active molecules, referred to as “postbiotics”. Postbiotics are defined as functional bioactive compounds obtained from food-grade microorganisms that confer health benefits when administered in adequate amounts. They include cell structures, secreted molecules or metabolic by-products, and inanimate microorganisms. This heterogeneous group of molecules presents a broad range of mechanisms and may exhibit some advantages over traditional “biotics” such as probiotics and prebiotics. Owing to the growing incidence of DM worldwide and the implications of the microbiota in the disease progression, postbiotics appear to be good candidates as novel therapeutic targets. In the present review, we summarise the current knowledge about postbiotic compounds and their potential application in diabetes management. Additionally, we envision future perspectives on this topic. In summary, the results indicate that postbiotics hold promise as a potential novel therapeutic strategy for DM.

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“…Studies have shown that childhood obesity increases the risk for adult T2D and coronary artery disease (CAD) [ 29 , 30 ]. Information on childhood BMI was not available for UKB participants but a related childhood measure of a comparative body size at age ten was available for 287,203 participants, among them 156,307 (54.42%) were females.…”

Section: Resultsmentioning

confidence: 99%

Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

Moldovan

Waldman²,

Brandes

et al. 2021

JPM

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One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

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Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2 alleles

Cited by 61 publications

References 75 publications

OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

Role of Postbiotics in Diabetes Mellitus: Current Knowledge and Future Perspectives

Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

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