Nasal DNA methylation at three CpG sites predicts childhood allergic disease

Breugel, Merlijn van; Qi, Cancan; Xu, Zhongli; Pedersen, Casper-Emil Tingskov; Petoukhov, Ilya; Vonk, Judith M.; Gehring, Ulrike; Berg, Marijn; Bügel, Marnix; Carpaij, Orestes A; Forno, Erick; Morin, Andréanne; Eliasen, Anders U.; Jiang, Yale; Berge, Maarten van den; Nawijn, Martijn C.; Li, Yang; Chen, Wei; Bont, Louis; Bønnelykke, Klaus; Celedón, Juan C.; Koppelman, Gerard H.; Xu, Cheng‐Jian

doi:10.1038/s41467-022-35088-6

Cited by 19 publications

(10 citation statements)

References 67 publications

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“…Numerous studies have, for example, linked epigenetic changes in blood or nasal epithelium/bronchial wash samples with allergic disease [40] and other complex respiratory diseases like chronic obstructive pulmonary disease [114]. Epigenetics‐based prediction models using a handful of markers are now appearing in the literature [115]. This is a very rapidly expanding field with huge potential for both diagnostics and novel treatment approaches.…”

Section: Beyond Genomicsmentioning

confidence: 99%

Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

et al. 2023

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Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease—both with regards to symptoms and underlying causal mechanisms—and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic‐based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases—including psychiatric disorders and allergies—available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high‐throughput molecular technologies, together with large collections of health data and novel data‐driven approaches, offers promise toward improved individual health through precision medicine.

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Section: Beyond Genomicsmentioning

confidence: 99%

Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

et al. 2023

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“…sound data of wheezes, image data from lung CT scans, or large-scale multi-omics data. [110][111][112] The extraction of relevant clinical features from EHRs using NLP has successfully diagnosed (childhood) asthma in discovery and replication cohorts. 111,113,114 In a study of a US birth cohort study, Seol et al 111 applied an AI algorithm to define asthma using established predictive and diagnostic criteria in 8196 children.…”

Section: Clinical Implementationmentioning

confidence: 99%

“…Additionally, several studies have investigated the potential of omics data for diagnosis. 112,115,116 One study developed an ML model that diagnosed IgE-sensitized allergic disease in 16-year-old children based on nasal cell DNA methylation of only three CpG sites. 112 External validation in an independent cohort indicated the prospect of reproducible epigenetic tests for diagnosis.…”

Section: Clinical Implementationmentioning

confidence: 99%

“…112,115,116 One study developed an ML model that diagnosed IgE-sensitized allergic disease in 16-year-old children based on nasal cell DNA methylation of only three CpG sites. 112 External validation in an independent cohort indicated the prospect of reproducible epigenetic tests for diagnosis. Alag 116 pursued a similar approach to diagnosing food allergy, where neural networks were trained on blood epigenetic markers.…”

Section: Clinical Implementationmentioning

confidence: 99%

“…The diagnosis or classification of allergic disease has been the area in which AI has been applied most, an exemplary case of supervised learning. ML has used a wide range of data sources to improve allergy or asthma diagnosis: text data from electronic health records (EHRs), sound data of wheezes, image data from lung CT scans, or large‐scale multi‐omics data 110–112 . The extraction of relevant clinical features from EHRs using NLP has successfully diagnosed (childhood) asthma in discovery and replication cohorts 111,113,114 .…”

Section: Current State Of Ai In the Allergy Research Fieldmentioning

confidence: 99%

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Current state and prospects of artificial intelligence in allergy

Breugel

Fehrmann

Bügel³

et al. 2023

Allergy

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The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

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Epigenomic insights into common human disease pathology

Bell

2024

Cell. Mol. Life Sci.

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The epigenome—the chemical modifications and chromatin-related packaging of the genome—enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological ‘clocks’ constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

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Nasal DNA methylation at three CpG sites predicts childhood allergic disease

Cited by 19 publications

References 67 publications

Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

Current state and prospects of artificial intelligence in allergy

Epigenomic insights into common human disease pathology

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