尽管在Aspen Lung哮喘会议近 60 年的历史中，人们对哮喘发病机制和表型的认识取得了长足的进步，但我们对该疾病的临床和分子异质性以及个体患者对治疗的反应的认识仍面临许多挑战。本报告总结了 2023 年阿斯彭肺会议的会议记录，会议旨在回顾哮喘的临床和分子异质性，更好地了解遗传、环境、细胞和分子影响因素对疾病易感性、异质性和严重性的影响。会议的目的是回顾有关哮喘表型、细胞过程以及疾病异质性和治疗反应的细胞特征的新信息。报告最后总结了我们对哮喘病理生物学认识的差距，并对未来研究提出了一些建议，以更好地了解哮喘疾病异质性的临床和基本机制，并推动这一日益严重的公共卫生问题的新疗法的开发。
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Figure 1. Schematic representation of the intersection of genetics and environmental exposures over the lifespan. Genetic factors are likely to
be of greater relevance in early life, with increasing contributions of the environment over time. The environment is likely to interact with the
genome through epigenetic mechanisms to broadly impact asthma heterogeneity and progression. The figure was created using BioRender.
RSV = respiratory syncytial virus; RV = rhinovirus.
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Figure 2. Airway epithelial cell diversity and function in the proximal and distal airways. The figure highlights the differences in distribution of
cell types in the proximal airway epithelium as compared with the distal airway epithelium. The marker genes expressed by each of the major
and rare cell types and their cellular function are highlighted in the accompanying table
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Figure 3. Airway smooth muscle (ASM) function and dysfunction in asthma. Canonical
contractile and relaxation functions of ASM integrate with asthma-associated signals, leading to
downstream phenotypic changes, including altered contraction and relaxation, hypertrophy
and hyperplasia, secretion of cytokines, and modified deposition of extracellular matrix. In
severe asthma, these changes in ASM phenotype can result in fixed airway obstruction
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Figure 4. Multiomics profiling of cells obtained via bronchoscopy can define new asthma subtypes and cellular mechanisms. Unsupervised
clustering from BAL samples can define profiles for healthy patients, characterized by IL-10–producing M2-like macrophages, and severe
asthma, exemplified here by IFN-g–producing CD4 and CD8 cells and IL-4–producing innate immune cells. Specific CD4 and CD8 phenotypes,
such as cytotoxic CD81 tissue resident memory cells, can be characterized using antibody-based profiling or single-cell sequencing. These
methods can be extended to characterize abnormalities in the airway epithelium in asthma in the context of specific immune cell populations
and phenotypes2.
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Figure 5. Schematic representation of the evolving complexities of the use of bioinformatics
and multiomics to interrogate disease pathogenesis and heterogeneity. Although most current
work relies on single intra-compartmental approaches, the full promise of bioinformatics awaits
the integration of multiple different cells, compartments, time sequencing, and treatment
effects.
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Figure 6. Illustration of type 2-low asthma with current understanding of phenotypes. A major
feature of type 2-low asthma is lack of T2 biomarkers, potentially due to effects of obesity,
metabolic dysfunction, neutrophilic inflammation, and/or use of high-dose oral or inhaled CSs.
Phenotypes under the type 2-low asthma umbrella include stable mild asthma, possibly mast
cell–driven and easily treated with ICS, and an early-onset, long-duration phenotype with few
markers of inflammation and airflow limitation with poor reversibility. Possible treatments include
bronchial thermoplasty and/or anti–TSLP; late-onset asthma in the setting of obesity characterized
by IL-1 and IL-6 and best treated with weight loss and possibly anti–IL-6 therapies; and a lateonset asthma phenotype characterized by sputum production and neutrophilic inflammation in the
setting of high pollution, smoking, and/or infection treated with smoking cessation and possibly
macrolides and/or pulmonary hygiene approaches. Adapted with permission from Reference 4.
BD = bronchodilator; BMI = body mass index; CSs = corticosteroids; ICS = inhaled corticosteroids;
Rx = treatment; T2 = type 2; TSLP = thymic stromal lymphopoietin
