Developmental profiles of eczema, wheeze, and rhinitis: two population-based birth cohort studies.
Developmental profiles of eczema, wheeze, and rhinitis: two population-based birth cohort studies.
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DOI：
10.1371/journal.pmed.1001748
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发表时间：
2014-10
期刊：
影响因子：
15.8
通讯作者：
Custovic A
中科院分区：
文献类型：
--
作者：
Belgrave DC;Granell R;Simpson A;Guiver J;Bishop C;Buchan I;Henderson AJ;Custovic A
利用两个人口出生队列的数据，Danielle Belgrave及其同事研究了过敏性疾病发展概况中特应性进展的证据。“特应性进行”一词被用来指童年时期从湿疹到哮喘和鼻炎的一系列症状的自然进展。我们假设这种表达不能充分描述儿童时期湿疹、喘息和鼻炎的自然史。我们提出，这种范式源于纵向研究的横断面分析，可能反映了一种可能在个体水平上不占主导地位的种群模式。来自两个以人口为基础的出生队列的9801名儿童的数据被用来确定湿疹、喘息和鼻炎的个体特征，以及这些症状的表现是否遵循特应性行军模式。儿童在1岁、3岁、5岁、8岁和11岁时进行评估。我们使用贝叶斯机器学习方法根据湿疹、喘息和鼻炎的个体特征识别不同的潜在类别。这种方法使我们能够随着时间的推移识别出具有相似湿疹、喘息和鼻炎模式的儿童群体。使用潜伏性疾病特征模型，数据被最好地描述为8个潜伏类别：无疾病（51.3%）、特应性湿疹（3.1%）、持续性湿疹和喘息（2.7%）、持续性湿疹合并后发性鼻炎（4.7%）、持续性喘息合并后发性鼻炎（5.7%）、短暂性喘息（7.7%）、仅湿疹（15.3%）和仅鼻炎（9.6%）。当对两个队列分别进行潜在变量建模时，得到了类似的结果。高度一致的致敏模式与湿疹、鼻炎和喘息的不同特征有关。本研究的主要局限性是用于确定两个队列中湿疹、喘息和鼻炎存在的问题措辞的差异。湿疹、喘息和鼻炎的发展特征是异质性的；只有一小部分儿童（约7%有症状的儿童）遵循类似特应性病程的轨迹。我们的免疫系统通过识别入侵者表面的特定分子，并启动一系列事件，最终导致病原体死亡，从而保护我们免受病毒、细菌和其他病原体的侵害。然而，有时我们的免疫系统会对无害的物质（如花粉等过敏原）产生反应，引发过敏或特应性症状。常见的特应性症状包括湿疹（皮肤上短暂的干燥发痒斑块）、喘息（胸部高音调的口哨，哮喘的症状）和鼻炎（在没有感冒或流感的情况下打喷嚏或流鼻涕）。所有这些症状在儿童时期都很常见，但最近的流行病学研究（对人口中疾病的模式和原因的检查）表明，受每种症状影响的儿童比例发生了与年龄相关的变化。例如，湿疹在婴儿中比在学龄儿童中更常见。这些发现导致了“特应性进行”的概念，即个体儿童症状的自然发展，从湿疹开始，然后发展到喘息，最后是鼻炎。特应性哮喘的概念引发了一些研究，旨在预防那些因患有湿疹而被认为有患哮喘风险的儿童患上哮喘。此外，一些指南建议临床医生告诉父母湿疹患儿以后可能会发展成哮喘或鼻炎。然而，由于流行病学研究的设计支持特应性进展的概念，患有湿疹的儿童后来发展为喘息和鼻炎实际上可能属于一个独特的儿童亚群，而不是代表特应性疾病的典型进展。了解特应性病程是否能充分描述儿童特应性疾病的自然病史，以避免对湿疹儿童采取不必要的策略来预防哮喘，这一点很重要。在这里，研究人员使用机器学习技术，通过考虑个体症状的时间相关（纵向）变化，在两个以人口为基础的大型出生队列中对儿童时期湿疹、喘息和鼻炎的发展概况进行建模。机器学习是一种数据驱动的方法，它使用对潜在变量（不能直接测量但从其他可观察特征推断出来的变量）的无监督学习来识别数据中的结构（例如，典型的症状进展）。研究人员使用了两个英国出生队列的数据——雅芳父母和儿童纵向研究（ALSPAC）和曼彻斯特哮喘和过敏研究（MAAS）——用于他们的研究（总共9801名儿童）。这两项研究都招募了出生时的儿童，并在定期复查诊所监测他们随后的健康状况。在每个回顾诊所，使用有效的问卷从父母那里收集有关湿疹、喘息和鼻炎的信息。然后，研究人员使用这些数据和机器学习方法来识别在生命的前11年中具有相似湿疹、喘息和鼻炎发作模式的儿童组。使用一种称为潜伏疾病概况模型的统计模型，研究人员发现数据最好地描述为8种潜伏类型：无疾病（51.3%），特应性march(3.1%)，持续性湿疹和喘息（2.7%），持续性湿疹伴晚发性鼻炎（4.7%），持续性喘息伴晚发性鼻炎（5.7%），短暂性喘息（7.7%），仅湿疹（15.3%）和仅鼻炎（9.6%）。这些发现表明，在英国的两个大型出生队列中，湿疹、喘息和鼻炎的发育概况是异质的。最值得注意的是，症状进展符合特应性进行曲的儿童不到7%。研究人员承认他们的研究有一些局限性。例如，在两个队列中，用于从父母那里收集孩子症状信息的问题措辞上的微小差异可能会轻微影响研究结果。然而，基于他们的研究结果，研究人员提出，由于湿疹、喘息和鼻炎是常见的，这些症状通常在个体中共存，但作为独立的实体，而不是作为症状的一个相关进展。因此，使用湿疹作为随后哮喘风险的指标并为湿疹儿童分配“预防”措施是有缺陷的。重要的是，临床医生需要了解儿童特应性疾病模式的异质性，并在向父母建议儿童特应性症状的发展和消退时，将这种差异性告知父母。请通过本摘要的在线版本（http://dx.doi.org/10.1371/journal.pmed.1001748）访问这些网站。英国国家健康服务选择网站提供有关湿疹（包括个人故事）、哮喘（包括个人故事）和鼻炎的信息。美国国家过敏和传染病研究所提供有关特应性疾病的信息。英国非营利组织Allergy UK提供有关特应性疾病的信息和对特应性疾病的描述。MedlinePlus百科全书有关于湿疹、喘息、和鼻炎（英语和西班牙语）MedlinePlus提供了有关过敏、湿疹和哮喘的更多资源的链接（英语和西班牙语）关于ALSPAC和MAAS的信息是可用的维基百科有关于机器学习和潜在疾病特征模型的页面（注意，维基百科是一个免费的在线百科全书，任何人都可以编辑；有多种语言版本）
Using data from two population-based birth cohorts, Danielle Belgrave and colleagues examine the evidence for atopic march in developmental profiles for allergic disorders. Please see later in the article for the Editors' Summary The term “atopic march” has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level. Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time. Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts. The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼7% of those with symptoms) follow trajectory profiles resembling the atopic march. Please see later in the article for the Editors' Summary Our immune system protects us from viruses, bacteria, and other pathogens by recognizing specific molecules on the invader's surface and initiating a sequence of events that culminates in the death of the pathogen. Sometimes, however, our immune system responds to harmless materials (allergens such as pollen) and triggers allergic, or atopic, symptoms. Common atopic symptoms include eczema (transient dry itchy patches on the skin), wheeze (high pitched whistling in the chest, a symptom of asthma), and rhinitis (sneezing or a runny nose in the absence of a cold or influenza). All these symptoms are very common during childhood, but recent epidemiological studies (examinations of the patterns and causes of diseases in a population) have revealed age-related changes in the proportions of children affected by each symptom. So, for example, eczema is more common in infants than in school-age children. These findings have led to the idea of “atopic march,” a natural progression of symptoms within individual children that starts with eczema, then progresses to wheeze and finally rhinitis. The concept of atopic march has led to the initiation of studies that aim to prevent the development of asthma in children who are thought to be at risk of asthma because they have eczema. Moreover, some guidelines recommend that clinicians tell parents that children with eczema may later develop asthma or rhinitis. However, because of the design of the epidemiological studies that support the concept of atopic march, children with eczema who later develop wheeze and rhinitis may actually belong to a distinct subgroup of children, rather than representing the typical progression of atopic diseases. It is important to know whether atopic march adequately describes the natural history of atopic diseases during childhood to avoid the imposition of unnecessary strategies on children with eczema to prevent asthma. Here, the researchers use machine learning techniques to model the developmental profiles of eczema, wheeze, and rhinitis during childhood in two large population-based birth cohorts by taking into account time-related (longitudinal) changes in symptoms within individuals. Machine learning is a data-driven approach that identifies structure within the data (for example, a typical progression of symptoms) using unsupervised learning of latent variables (variables that are not directly measured but are inferred from other observable characteristics). The researchers used data from two UK birth cohorts—the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Manchester Asthma and Allergy Study (MAAS)—for their study (9,801 children in total). Both studies enrolled children at birth and monitored their subsequent health at regular review clinics. At each review clinic, information about eczema, wheeze, and rhinitis was collected from the parents using validated questionnaires. The researchers then used these data and machine learning methods to identify groups of children with similar patterns of onset of eczema, wheeze, and rhinitis over the first 11 years of life. Using a type of statistical model called a latent disease profile model, the researchers found that the data were best described by eight latent classes—no disease (51.3% of the children), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). These findings show that, in two large UK birth cohorts, the developmental profiles of eczema, wheeze, and rhinitis were heterogeneous. Most notably, the progression of symptoms fitted the profile of atopic march in fewer than 7% of children with symptoms. The researchers acknowledge that their study has some limitations. For example, small differences in the wording of the questions used to gather information from parents about their children's symptoms in the two cohorts may have slightly affected the findings. However, based on their findings, the researchers propose that, because eczema, wheeze, and rhinitis are common, these symptoms often coexist in individuals, but as independent entities rather than as a linked progression of symptoms. Thus, using eczema as an indicator of subsequent asthma risk and assigning “preventative” measures to children with eczema is flawed. Importantly, clinicians need to understand the heterogeneity of patterns of atopic diseases in children and to communicate this variability to parents when advising them about the development and resolution of atopic symptoms in their children. Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001748. The UK National Health Service Choices website provides information about eczema (including personal stories), asthma (including personal stories), and rhinitis The US National Institute of Allergy and Infectious Diseases provides information about atopic diseases The UK not-for-profit organization Allergy UK provides information about atopic diseases and a description of the atopic march MedlinePlus encyclopedia has pages on eczema, wheezing, and rhinitis (in English and Spanish) MedlinePlus provides links to further resources about allergies, eczema, and asthma (in English and Spanish) Information about ALSPAC and MAAS is available Wikipedia has pages on machine learning and latent disease profile models (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
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影响因子：
6.1
作者：
Kulig, M;Bergmann, R;Wahn, U
通讯作者：
Wahn, U
影响因子：
14.2
作者：
Illi, S;von Mutius, E;Wahn, U
通讯作者：
Wahn, U
影响因子：
5.2
作者：
Austin, JB;Kaur, B;Warner, JO
通讯作者：
Warner, JO
影响因子：
12.4
作者：
Lazic, N.;Roberts, G.;Simpson, A.
通讯作者：
Simpson, A.
影响因子：
12.4
作者：
Kurukulaaratchy, RJ;Matthews, S;Arshad, SH
通讯作者：
Arshad, SH