The association of prenatal risk factors with childhood asthma

Winds of change a tale of: asthma and microbiome

The role of the microbiome in asthma is highlighted, considering its influence on immune responses and its connection to alterations in asthmatic patients. In this context, we review the variables influencing asthma phenotypes from a microbiome perspective and provide insights into the microbiome's role in asthma pathogenesis. Previous cohort studies in patients with asthma have shown that the presence of genera such as Bifidobacterium, Lactobacillus, Faecalibacterium, and Bacteroides in the gut microbiome has been associated with protection against the disease. While, the presence of other genera such as Haemophilus, Streptococcus, Staphylococcus, and Moraxella in the respiratory microbiome has been implicated in asthma pathogenesis, indicating a potential link between microbial dysbiosis and the development of asthma. Furthermore, respiratory infections have been demonstrated to impact the composition of the upper respiratory tract microbiota, increasing susceptibility to bacterial diseases and potentially triggering asthma exacerbations. By understanding the interplay between the microbiome and asthma, valuable insights into disease mechanisms can be gained, potentially leading to the development of novel therapeutic approaches.

Asthma in developing countries in the Asia-Pacific Region (APR)

There is growing interest in the epidemiology of asthma in developing countries, especially in the Asia-Pacific Region (APR). A number of reviews have been published in this field, but a comprehensive synthesis of overall data has not been reported. Here, we summarized the burden, risk factors and challenges of asthma management in developing countries with a specific emphasis on the APR by consolidating evidence from both systematic and narrative reviews published up until February 2023. We found that although asthma prevalence in low and low-middle-income countries (LMICs) is known to be generally lower compared to high-income countries, the burden is substantially greater. Studies conducted in APR LMIC have reported a range of risk factors, including pre- and post-natal factors, environmental considerations, lifestyle measures, individual features and genetics. The low and inequitable distribution of quality preventive and curative health care, a lack of advanced diagnostic measures, non-availability and non-affordability of novel therapeutics, cultural beliefs and practices, and diverse disease phenotypes make it challenging to achieve optimal asthma control in the region. Hence, we call for the development of a region-specific blueprint for action to mitigate this challenging situation, to help reduce the burden of asthma in APR LMIC.

Indoor environment in relation to recurrent childhood asthma in Yancheng, China: a hospital-based case-control study

This investigation explored the association between indoor environmental factors and childhood asthma in Yancheng, China. Asthma case (201 children with recurrent asthma) and control cohorts (242 healthy subjects) were recruited from a Traditional Chinese Medical (TCM) Hospital in Yancheng city, based on the results of an ISAAC questionnaire. Questionnaires regarding environmental risk factors were completed by the child's primary caregivers. To compare data on environmental VOCs and formaldehyde contents between asthma and control cohorts, we passively conducted a 10-day indoor and outdoor sampling. Breastfeeding was a major protective indoor environmental factor for recurrent asthma (adjusted odds ratio [aOR]: 0.368, 95% confidence interval [CI]: 0.216-0.627). Our analysis revealed that childhood recurrent asthma was intricately linked to a family history of asthma. Recurrent asthma was also associated with passive smoking [aOR2.115 (95%-CI 1.275-3.508)]. Analogous correlations were observed between household renovation or new furniture introduction and recurrent asthma [aOR3.129(95%-CI1.542-6.347)]. Benzene and formaldehyde were present in all examined homes. Enhanced benzene and formaldehyde concentrations were strongly evident among asthma versus control cohorts, and they were strongly correlated with augmented recurrent asthma risk. Home environment heavily regulates incidences of childhood recurrent asthma. Hence, actions against the indoor environmental risk factors described in this study may assist in the prevention of recurrent asthma among children.

Risk factors of asthma in the Asian population: a systematic review and meta-analysis

Background and objective

An increasing trend of asthma prevalence was observed in Asia; however, contributions of environmental and host-related risk factors to the development of this disease remain uncertain. This study aimed to perform a systematic review and meta-analysis for asthma-associated risk factors reported in Asia.

Methods

We systematically searched three public databases (Web of Science, PubMed, and Scopus) in Feb 2021. We only included articles that reported environmental and host-related risk factors associated with asthma in the Asian population. Random-effect meta-analyses were conducted for frequently reported asthma-associated risk factors to provide an overall risk estimate of asthma development.

Results

Of 4030 records obtained from public databases, 289 articles were selected for review. The most frequently reported asthma-associated risk factor was the family history of allergy-related conditions. The random-effect asthma risk estimates (pooled odds ratio, OR) were 4.66 (95% confidence interval (CI): 3.73–5.82) for the family history of asthma, 3.50 (95% CI: 2.62–4.67) for the family history of atopy, 3.57 (95% CI: 3.03–4.22) for the family history of any allergic diseases, 1.96 (95% CI: 1.47–2.61) for the family history of allergic rhinitis, and 2.75 (95% CI: 1.12–6.76) for the family history of atopic dermatitis. For housing-related factors, including the presence of mold, mold spots, mold odor, cockroach, water damage, and incense burning, the random-effect pooled OR ranged from 1.43 to 1.73. Other risk factors with significant pooled OR for asthma development included male gender (1.30, 95% CI: 1.23–1.38), cigarette smoke exposure (1.44, 95% CI: 1.30–1.60), cigarette smoking (1.66, 95% CI: 1.44–1.90), body mass index (BMI)–related parameters (pooled OR ranged from 1.06 to 2.02), various types of air pollution (NO₂, PM10, and O₃; pooled OR ranged from 1.03 to 1.22), and pre- and perinatal factors (low birth weight, preterm birth, and cesarean section; pooled OR ranged from 1.14 to 1.32).

Conclusions

The family history of asthma was the most frequently reported risk factor for asthma development in Asia with the highest risk estimate for asthma development. This suggests a major role of the genetic component in asthma pathogenesis. Further study on asthma genetics is required to improve the current understanding of asthma etiology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40101-021-00273-x.

Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child's environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors.