Achieving Precision Medicine in Allergic Disease: Progress and Challenges

Abstract

Allergic diseases (atopic dermatitis, food allergy, eosinophilic esophagitis, asthma and allergic rhinitis), perhaps more than many other traditionally grouped disorders, share several overlapping inflammatory pathways and risk factors, though we are still beginning to understand how the relevant patient and environmental factors uniquely shape each disease. Precision medicine is the concept of applying multiple levels of patient-specific data to tailor diagnoses and available treatments to the individual; ideally, a patient receives the right intervention at the right time, in order to maximize effectiveness but minimize morbidity, mortality and cost. While precision medicine in allergy is in its infancy, the recent success of biologics, development of tools focused on large data set integration and improved sampling methods are encouraging and demonstrates the utility of refining our understanding of allergic endotypes to improve therapies. Some of the biggest challenges to achieving precision medicine in allergy are characterizing allergic endotypes, understanding allergic multimorbidity relationships, contextualizing the impact of environmental exposures (the “exposome”) and ancestry/genetic risks, achieving actionable multi-omics integration, and using this information to develop adequately powered patient cohorts and refined clinical trials. In this paper, we highlight several recently developed tools and methods showing promise to realize the aspirational potential of precision medicine in allergic disease. We also outline current challenges, including exposome sampling and building the “knowledge network” with multi-omics integration.

Associations of Four sensitization patterns revealed by Latent Class Analysis with Clinical symptoms: A multi-center study of China

“… 15 , 17 Furthermore, to better disclose the underlying complicated sensitization patterns of common allergens depending on low-cost test data, we utilized a more robust statistic method, the latent class analysis (LCA), to explore the heterogeneity of potential sensitization patterns. 8 Besides, since the clinical information of participants was collected through face-to-face routine medical investigation, some recall bias might exist in our study. Additionally, the cross-sectional study design could not certify the causal relationship between exposure and outcomes, as well as calculate the risk ratios (RRs) of exposure.…”

“… 7 Given conventional analysis for co-sensitization characteristics using the Venn diagram couldn't disclose the complexity of sensitization patterns, many researchers have begun to utilize some more robust statistic methods for exploring the heterogeneity of potential sensitization patterns. 8 …”

Mapping Allergic Diseases in Sub-Saharan Africa

“…In constant increase globally, allergies and allergic diseases are no longer "a rare fact" in Africa as we thought (1,2). The International Study of Asthma and Allergies in Childhood Phase III that included 16 African countries (Algeria, Cameroon, Congo, Congo (RDC), Ethiopia, Gabon, Ivory Coast, Kenya, Morocco, Nigeria, Reunion Island, South Africa, and Tunisia) (1) and a systematic review based on data from 11 African countries (including Botswana, Democratic Republic of Congo, Ghana, Kenya, Morocco, Mozambique, Nigeria, South Africa, Tanzania, Tunisia, and Zimbabwe) (2), highlight that allergies are emerging disease in Sub-Saharan Africa. In 1999, a study realized on suspected allergy cases in Côte d'Ivoire showed that 56.4%, 30.7%, 23.5%, 8.5%, 2.8%, and 1.4% were, respectively, sensitized to mites, cockroaches, molds, pets' danders (dog and cat), foods (rice, peanut, and soy) and latex allergens.…”

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

“…There has been a lack of omics studies conducted among minority populations who bear a disproportionally high burden and incidence of asthma (almost twice as high in AAs (16%) compared to European Americans (8.2%) [204]. Additional efforts are needed to widen the diversity of multi-omics databases and develop analytical methods, including deep learning, to analyze, annotate, and integrate multi-omics data to inform precision medicinebased decision making [205]. New bioinformatic tools for data analysis are imperative given the large volume and complexity of available data toward the road of multi-omics application in precision medicine.…”