Genetic differences according to onset age and lung function in asthma: A cluster analysis

Kim, Han‐Kyul; Kang, Ji‐One; Lim, Ji Eun; Ha, Tae-Woong; Jung, Hae Un; Lee, Wonjun; Kim, Dong Jun; Baek, Eun Ju; Adcock, Ian M.; Chung, Kian Fan; Kim, Tae‐Bum; Oh, Bermseok

doi:10.1002/clt2.12282

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…CYP3A5*3/*3 and CYP3A4*22 genotypes were found to be linked to ICS response [ 54 , 55 ]. These studies provide evidence that genetic variations greatly affect ICS responsiveness, although the exact relationships between genetic variations and ICS response remain largely unknown [ 36 , 56 , 57 ]. Currently, many candidate genes are being studied, and pharmacogenetics has not yet reached routine clinical practice in asthma care.…”

Section: Introductionmentioning

confidence: 99%

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Nkoy,

Stone,

Zhang

et al. 2024

JMIR Med Inform

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Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

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Section: Introductionmentioning

confidence: 99%

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Nkoy,

Stone,

Zhang

et al. 2024

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…The pathogenesis of asthma is relatively complex and not fully elucidated. Research on the pathogenesis of asthma has focused mainly on chronic inflammation of the airways, airway hyperresponsiveness [ 7 ], airway remodeling [ 8 ], and genetic mechanisms [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of global gene expression using RNA-sequencing reveals novel mechanism of Yanghe Pingchuan decoction in the treatment of asthma

Pan,

He,

Gong

et al. 2024

BMC Pulm Med

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Background Yanghe Pingchuan decoction (YPD) has been used for asthma treatment for many years in China. We sought to understand the mechanism of YPD, and find more potential targets for YPD-based treatment of asthma. Methods An ovalbumin-induced asthma model in rats was created. Staining (hematoxylin and eosin, Masson) was used to evaluate the treatment effect of YPD. RNA-sequencing was carried out to analyze global gene expression, and differentially expressed genes (DEGs) were identified. Analysis of the functional enrichment of genes was done using the Gene Ontology database (GO). Analysis of signaling-pathway enrichment of genes was done using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Real-time reverse transcription-quantitative polymerase chain reaction was undertaken to measure expression of DEGs. Results Pathology showed that YPD had an improvement effect on rats with asthma. RNA-sequencing showed that YPD led to upregulated and downregulated expression of many genes. The YPD-based control of asthma pathogenesis may be related to calcium ion (Ca2+) binding, inorganic cation transmembrane transporter activity, microtubule motor activity, and control of canonical signaling (e.g., peroxisome proliferator-activated receptor, calcium, cyclic adenosine monophosphate). Enrichment analyses suggested that asthma pathogenesis may be related to Ca2 + binding and contraction of vascular smooth muscle. A validation experiment showed that YPD could reduce the Ca2 + concentration by inhibiting the Angiopoietin-II (Ang-II)/Phospholipase (PLA)/calmodulin (CaM0 signaling axis. Conclusion Control of asthma pathogenesis by YPD may be related to inhibition of the Ang-II/PLA/CaM signaling axis, reduction of the Ca2+ concentration, and relaxation of airway smooth muscle (ASM).

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Genetic interactions and pleiotropy in metabolic diseases: Insights from a comprehensive GWAS analysis

Shen,

Pan,

et al. 2024

J Cellular Molecular Medi

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This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome‐wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High‐Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two‐sample Mendelian randomization analyses, analysis under the multiplicity‐complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co‐localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones.

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Genetic differences according to onset age and lung function in asthma: A cluster analysis

Cited by 4 publications

References 54 publications

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Analysis of global gene expression using RNA-sequencing reveals novel mechanism of Yanghe Pingchuan decoction in the treatment of asthma

Genetic interactions and pleiotropy in metabolic diseases: Insights from a comprehensive GWAS analysis

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