Asthma Research Research Articles

Assessment of Co-Sensitization between Pollen and Food Allergen Sources among Bengali Population, West Bengal, India

Introduction: Secondary food allergies due to cross-reactivity between pollen and food allergens are a significant health burden in India. Prevalence of cross-reactivity varies by region, and particularly in West Bengal, it remains unexplored. The present study investigated sensitivities of pollen-related food allergy (PFA) patients of West Bengal by empirically determining co-sensitization patterns. Methods: The present study retrospectively analysed 1,310 pollen-allergic (PA) patients from the Allergy and Asthma Research Centre, West Bengal. A questionnaire survey was performed to record patients’ demographic and clinical features and categorized into seasonal and perennial ones. All patients were subjected to the skin prick test and specific IgE for evaluation of sensitivity against 16 pollen and 16 food allergens. Co-relation and principal component analysis (PCA) was performed to determine co-sensitivity. Results: Seasonal allergic patients were comparatively higher than perennial ones. PFA patients were mostly sensitive to eggplant (n = 387), while Azadirachta indica (n = 341) was predominant sensitizer among pollen allergen sources. Cynodon dactylon showed maximum significant positive correlation with banana, orange, peanut, pineapple, cucumber, eggplant, potato, tomato, and wheat. In the seasonal group, Cynodon dactylon-specific IgE in PFA patients (median = 4.60 kU/L) was significantly higher from that of PA ones (median = 3.44 kU/L). Among 32 allergens tested, 27 were placed in first two dimensions of the PCA biplot. Conclusion: The present study established that co-sensitivity between pollen and food allergen sources were predominant in allergic patients of West Bengal. The co-sensitization patterns among unrelated allergen sources may be due to possible expression of biologic cross-reactivity to similar allergens.

Research hotspot and frontier analysis of traditional Chinese medicine in asthma using bibliometric methods from 1991 to 2021.

Traditional Chinese medicine (TCM) has been successfully used to treat asthmatic conditions for centuries. Nevertheless, the current hotspots and research frontiers on TCM for asthma have not been systematically investigated on the basis of bibliometric analysis. In this study, a scientometric analysis (1991-2021) was carried out on 3081 journal articles obtained from the Web of Science Core Collection database to explore the basic characteristics, research hotspots, and frontiers of TCM in asthma research. The results revealed the following: (1) Research on TCM in asthma has received widespread attention since the beginning of the 21st century; perhaps 2009 was an important turning point. TCM in asthma research shows a trend of interdisciplinary development. (2) Well-known universities/institutions in China, the United States, and South Korea have conducted the main body of current TCM research in asthma. JingCheng Dong (Fudan University, China) and XiuMin Li (Mount Sinai School of Medicine, USA) are the top 2 leading authors in this field. However, there is still a lack of international cooperation in the field of TCM in asthma research, and the influence of researchers in China and South Korea still needs improvement. (3) The Journal of Allergy and Clinical Immunology ranks first in the research field on the influence of TCM in asthma. (4) Hotspots and frontiers of TCM in asthma are provided according to the timeline analyses of the research. In the former, complementary medicine, alternative treatment, allergic rhinitis, airway remodeling, Dietary Approach to Stop Hypertension diet, and eosinophilic esophagitis are in the exploratory stage. In the latter, pharmacology, essential oil, gut microbiota, and oxidative stress were investigated from 2006 until late 2021 as period B, which contradicts period A(1991-2005). Moreover, limitations of this bibliometric analysis and the study of TCM research in asthma still exist, which are sufficiently important to warrant further investigations. Finally, we propose the significant importance of the real quintessence and characteristics of TCM in clinical and future research.

Skeletal Muscle Adiposity and Lung Function Trajectory in the Severe Asthma Research Program.

Rationale: Extrapulmonary manifestations of asthma, including fatty infiltration in tissues, may reflect systemic inflammation and influence lung function and disease severity. Objectives: To determine if skeletal muscle adiposity predicts lung function trajectory in asthma. Methods: Adult SARP III (Severe Asthma Research Program III) participants with baseline computed tomography imaging and longitudinal postbronchodilator FEV1% predicted (median follow-up 5 years [1,132 person-years]) were evaluated. The mean of left and right paraspinous muscle density (PSMD) at the 12th thoracic vertebral body was calculated (Hounsfield units [HU]). Lower PSMD reflects higher muscle adiposity. We derived PSMD reference ranges from healthy control subjects without asthma. A linear multivariable mixed-effects model was constructed to evaluate associations of baseline PSMD and lung function trajectory stratified by sex. Measurements and Main Results: Participants included 219 with asthma (67% women; mean [SD] body mass index, 32.3 [8.8] kg/m2) and 37 control subjects (51% women; mean [SD] body mass index, 26.3 [4.7] kg/m2). Participants with asthma had lower adjusted PSMD than control subjects (42.2 vs. 55.8 HU; P < 0.001). In adjusted models, PSMD predicted lung function trajectory in women with asthma (β = -0.47 Δ slope per 10-HU decrease; P = 0.03) but not men (β = 0.11 Δ slope per 10-HU decrease; P = 0.77). The highest PSMD tertile predicted a 2.9% improvement whereas the lowest tertile predicted a 1.8% decline in FEV1% predicted among women with asthma over 5 years. Conclusions: Participants with asthma have lower PSMD, reflecting greater muscle fat infiltration. Baseline PSMD predicted lung function decline among women with asthma but not men. These data support an important role of metabolic dysfunction in lung function decline.

Disruption in Bio-Psycho-Social Context: Children’s Perceptions of the COVID-19 Pandemic in Aotearoa New Zealand

ABSTRACT Children growing up during the COVID-19 pandemic have seen unprecedented restructuring of their childhoods through lockdowns, virtual schooling and other public health measures. Theories of biographical disruption developed from individual experiences of life-altering diagnoses predict that unforeseen events such as the pandemic will restructure individual perceptions of their future life narrative. Such theories have been developed from adult experiences, however, with scholars suggesting that normalcy may be more salient to children’s experience of chronic illness. Children’s experiences might be expected to vary from those of adults’ due to their different structural position and younger life history which shifts children’s perceptions of temporality, normalcy and disruption. Empirical evidence from young people with chronic illness, meanwhile, describes diverse experiences of continuity and disruption, while the rhythms and interruptions of childhood chronic illness remain without an adequate explanatory framework. Aotearoa New Zealand’s zero-COVID approach presents a unique opportunity to understand children’s perceptions of disruption and continuity. I worked with 26 children aged 7–11 living in diverse locations in Auckland, developing a comic-based method to elicit children’s perspectives and co-construct narratives over virtual or in-person research visits. Juxtaposed with caregiver experiences and paediatric asthma research, I analyse children’s perspectives to suggest how children differently make sense of and accommodate crisis events. I argue that moving beyond biographical disruption to address the bio-psycho-social factors producing diverse ruptures, discontinuities and interferences will more completely represent children’s experiences of chronic illness and life crises.

What have we learnt from real-life research in asthma and COPD? Standards and novel designs for the future.

What have we learnt from real-life research in asthma and COPD? Standards and novel designs for the future.

Urinary total conjugated 3-bromotyrosine, asthma severity, and exacerbation risk.

Asthma is an inflammatory disease of the airways characterized by eosinophil recruitment, eosinophil peroxidase release, and protein oxidation through bromination, which following tissue remodeling results in excretion of 3-bromotyrosine. Predicting exacerbations and reducing their frequency is critical for the treatment of severe asthma. In this study, we aimed to investigate whether urinary total conjugated bromotyrosine can discriminate asthma severity and predict asthma exacerbations. We collected urine from participants with severe (n = 253) and nonsevere (n = 178) asthma, and the number of adjudicated exacerbations in 1-yr longitudinal follow-up was determined among subjects enrolled in the Severe Asthma Research Program, a large-scale National Institutes of Health (NIH)-funded consortium. Urine glucuronidated bromotyrosine and total conjugated forms were quantified by hydrolysis with either glucuronidase or methanesulfonic acid, respectively, followed by liquid chromatography-tandem mass spectrometry analyses of free 3-bromotyrosine. Blood and sputum eosinophils were also counted. The majority of 3-bromotyrosine in urine was found to exist in conjugated forms, with glucuronidated bromotyrosine representing approximately a third, and free bromotyrosine less than 1% of total conjugated bromotyrosine. Total conjugated bromotyrosine was poorly correlated with blood (r2 = 0.038) or sputum eosinophils (r2 = 0.0069). Compared with participants with nonsevere asthma, participants with severe asthma had significantly higher urinary total conjugated bromotyrosine levels. Urinary total conjugated bromotyrosine was independently associated with asthma severity, correlated with the number of asthma exacerbations, and served as a predictor of asthma exacerbation risk over 1-yr of follow-up.

Low CC16 mRNA Expression Levels in Bronchial Epithelial Cells Are Associated with Asthma Severity.

Rationale: CC16 is a protein mainly produced by nonciliated bronchial epithelial cells (BECs) that participates in host defense. Reduced CC16 protein concentrations in BAL and serum are associated with asthma susceptibility. Objectives: Few studies have investigated the relationship between CC16 and asthma progression, and none has focused on BECs. In this study, we sought to determine if CC16 mRNA expression levels in BECs are associated with asthma severity. Methods: Association analyses between CC16 mRNA expression levels in BECs (242 asthmatics and 69 control subjects) and asthma-related phenotypes in Severe Asthma Research Program were performed using a generalized linear model. Measurements and Main Results: Low CC16 mRNA expression levels in BECs were significantly associated with asthma susceptibility and asthma severity, high systemic corticosteroids use, high retrospective and prospective asthma exacerbations, and low pulmonary function. Low CC16 mRNA expression levels were significantly associated with high T2 inflammation biomarkers (fractional exhaled nitric oxide and sputum eosinophils). CC16 mRNA expression levels were negatively correlated with expression levels of Th2 genes (IL1RL1, POSTN, SERPINB2, CLCA1, NOS2, and MUC5AC) and positively correlated with expression levels of Th1 and inflammation genes (IL12A and MUC5B). A combination of two nontraditional T2 biomarkers (CC16 and IL-6) revealed four asthma endotypes with different characteristics of T2 inflammation, obesity, and asthma severity. Conclusions: Our findings indicate that low CC16 mRNA expression levels in BECs are associated with asthma susceptibility, severity, and exacerbations, partially through immunomodulation of T2 inflammation. CC16 is a potential nontraditional T2 biomarker for asthma development and progression.

A Multi-Point View of Genetic Factors Affecting Hereditary Transmissibility of Asthma.

Asthma is a respiratory illness characterized by episodes of bronchial spasm that make breathing difficult. It often has an association with an allergic response or any hypersensitivity followed by inflammation and hypersensitivity of the airways. Asthma is a sophisticated, complicated, and diverse respiratory condition. Even while heredity is a chief hazard factor for developing asthma, environmental and inner exposures, as well as how they interact with genetic variables, also have a substantial impact on the disease's pathogenesis. In recent years, the field of asthma research has seen the emergence and deployment of high throughput omics techniques for the unbiased screening of biomarkers such as genes, transcripts, proteins, and metabolites. Better asthma risk profile has been achieved by utilizing large-scale studies that are illustrative of various population-based data and merging with clinical data. This allergic airway illness has a wide range of phenotypes and endotypes, many of which have not yet been fully defined. The diversity of phenotypes is reflected in similarly complicated etiologies, and it is thought that a number of genetic and environmental variables interrelate to promote the danger of asthma in both children and adults. In order to achieve this objective, significant efforts are necessary to describe different phenotypes, uncover fundamental mechanisms, and find associated biomarkers. It is clear that the analysis, monitoring, and treatment of asthma require a tailored strategy. The risk of developing a multifactorial ailment is characterized by the grade of genetic relationship between the individual and affected relative. Additionally, the risk is typically larger if the relative has a severe illness or was afflicted when they were young. The asthma phenotype is highly varied and expressed non-linearly in contrast to single gene illnesses. This makes it challenging to predict whether someone with a certain gene or set of genotypes would have asthma.

Childhood asthma and type 1 diabetes mellitus: A meta-analysis and bidirectional Mendelian randomization study.

Worldwide incidence and prevalence of both asthma and type 1 diabetes mellitus (T1DM) in children have been increasing in past decades. Association between the two diseases has been found in some but not in other studies. We conducted a meta-analysis to verify such an association, and bidirectional Mendelian randomization analysis to examine the potential cause-effect relationships. Three databases (PubMed, Embase, and Web of Science) were searched from their inception to February 1, 2021. Pooled hazard ratios (HR) or odds ratios (OR), and 95% confidence intervals, were calculated. Associations between single-nucleotide polymorphisms with childhood asthma and T1DM were selected based on genome-wide association studies. The outcome datasets were obtained from FinnGen study. We used the inverse-variance-weighted (IVW), weighted median and MR-Egger methods to estimate causal effects. To assess robustness and horizontal pleiotropy, MR-Egger regression and MR pleiotropy residual sum and outlier test were conducted. In meta-analysis, childhood asthma was associated with an increased risk of T1DM (HR=1.30, 95% CI 1.05-1.61, P=.014), whereas T1DM was not associated with the risk of asthma (HR=0.98, 95% CI 0.64-1.51, P=.941; OR=0.84, 95% CI 0.65-1.08, P=.168). MR analysis indicated increased genetic risk of T1DM in children with asthma (OR=1.308; 95% CI 1.030-1.661; P=.028). Analysis using the IVW method indicated no association between T1DM and genetic risk of asthma (OR=1.027, 95%CI 0.970-1.089, P=.358). Both meta-analysis and MR study suggested that childhood asthma was a risk factor for T1DM. No epidemiological or genetic evidence was found for an association of T1DM with asthma incidence. Further studies could be carried out to leverage this newfound insight into better clinical and experimental research in asthma and T1DM.

Research advances in airway remodeling in asthma: a narrative review.

Background and ObjectiveAsthma is a common chronic disorder of the airway, and its disability and mortality rates continue to increase each year. Due to the lack of an ideal treatment, asthma control in China remains unsatisfactory. Airway remodeling is the pathological basis for the eventual development of the fixed airflow limitation in asthmatic patients. Early diagnosis and the prevention of airway remodeling has the potential to decrease disease severity, to improve control, and to prevent disease expression.MethodsThis article presents an overview. The literature was combed through via CNKi and PubMed according to the listed keywords. We considered Chinese and English original publications (basic science and clinical), reviews and abstracts of 21th Century.Key Content and FindingsWe review the pathological features and pathogenesis of, and the interventional treatment options for airway remodeling in asthmatic patients, emphasizing the importance of airway remodeling in asthma and providing novel insights into the prevention and control of asthma.ConclusionsThus, there have been research advances in airway remodeling, especially in the areas of slowing down or reversing airway remodeling. As growing studies showed, treating airway remodeling is a promising strategy in preventing the occurrence and progression of asthma. Breakthroughs in these difficulties airway remodeling still facing will open up new avenues in the research and treatment of asthma.

Determinants of lung function across childhood in the Severe Asthma Research Program (SARP) 3

Children with asthma are at risk for low lung function extending into adulthood, but understanding of clinical predictors is incomplete. We sought to determine phenotypic factors associated with FEV1 throughout childhood in the Severe Asthma Research Program 3 pediatric cohort. Lung function was measured at baseline and annually. Multivariate linear mixed-effects models were constructed to assess the effect of baseline and time-varying predictors of prebronchodilator FEV1 at each assessment for up to 6 years. All models were adjusted for age, predicted FEV1 by Global Lung Function Initiative reference equations, race, sex, and height. Secondary outcomes included postbronchodilator FEV1 and prebronchodilator FEV1/forced vital capacity. A total of 862 spirometry assessments were performed for 188 participants. Factors associated with FEV1 include baseline Feno (B, -49 mL/log2 PPB; 95% CI, -92 to -6), response to a characterizing dose of triamcinolone acetonide (B, -8.4 mL/1% change FEV1 posttriamcinolone; 95% CI, -12.3 to -4.5), and maximal bronchodilator reversibility (B, -27 mL/1% change postbronchodilator FEV1; 95% CI, -37 to -16). Annually assessed time-varying factors of age, obesity, and exacerbation frequency predicted FEV1 over time. Notably, there was a significant age and sex interaction. Among girls, there was no exacerbation effect. For boys, however, moderate (1-2) exacerbation frequency in the previous 12 months was associated with -20 mL (95% CI, -39 to -2) FEV1 at each successive year. High exacerbation frequency (≥3) 12 to 24 months before assessment was associated with -34 mL (95% CI, -61 to -7) FEV1 at each successive year. In children with severe and nonsevere asthma, several clinically relevant factors predict FEV1 over time. Boys with recurrent exacerbations are at high risk of lower FEV1 through childhood.

Changing the Way Babies Eat: Supporting Early Allergen Feeding Around the World

This symposium occurred during the European Academy of Allergy and Clinical Immunology (EAACI) Congress, 2022. Angela Claver Monzón from the Hospital Universitari Quiron Dexeus, Barcelona, Spain, welcomed attendees and gave a brief introduction to the topic of food allergy prevention in children. Andrea Mikkelsen from the University of Gothenburg, Sweden, highlighted the importance of diet diversity and the early introduction of food allergens to infants before discussing some of the challenges associated with this. Kari Nadeau from The Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, California, USA, described the results from a study that evaluated different strategies for the early introduction of food allergens. She highlighted that consumption of even small quantities of a multi-allergen mixture for 1 year was associated with improvements in subsequent food challenge reactivity compared with single or double food introductions. Wendy Sue Swanson, also from The Sean N. Parker Center for Allergy and Asthma Research and Before Brands, Inc., outlined the development of a 16-allergen mixture and described the rationale and design of the INTENT study, which will evaluate the potential benefits of this product to support early food allergen introduction. The symposium concluded with a live question and answer session.

Long COVID: which symptoms can be attributed to SARS-CoV-2 infection?

Long COVID: which symptoms can be attributed to SARS-CoV-2 infection?

Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future.

Real-world evidence from multinational disease registries is becoming increasingly important not only for confirming the results of randomised controlled trials, but also for identifying phenotypes, monitoring disease progression, predicting response to new drugs and early detection of rare side-effects. With new open-access technologies, it has become feasible to harmonise patient data from different disease registries and use it for data analysis without compromising privacy rules. Here, we provide a blueprint for how a clinical research collaboration can successfully use real-world data from existing disease registries to perform federated analyses. We describe how the European severe asthma clinical research collaboration SHARP (Severe Heterogeneous Asthma Research collaboration, Patient-centred) fulfilled the harmonisation process from nonstandardised clinical registry data to the Observational Medical Outcomes Partnership Common Data Model and built a strong network of collaborators from multiple disciplines and countries. The blueprint covers organisational, financial, conceptual, technical, analytical and research aspects, and discusses both the challenges and the lessons learned. All in all, setting up a federated data network is a complex process that requires thorough preparation, but above all, it is a worthwhile investment for all clinical research collaborations, especially in view of the emerging applications of artificial intelligence and federated learning.

Anti-nucleocapsid antibody levels and pulmonary comorbid conditions are linked to post-COVID-19 syndrome.

BACKGROUNDProlonged symptoms after SARS-CoV-2 infection are well documented. However, which factors influence development of long-term symptoms, how symptoms vary across ethnic groups, and whether long-term symptoms correlate with biomarkers are points that remain elusive.METHODSAdult SARS-CoV-2 reverse transcription PCR–positive (RT-PCR–positive) patients were recruited at Stanford from March 2020 to February 2021. Study participants were seen for in-person visits at diagnosis and every 1–3 months for up to 1 year after diagnosis; they completed symptom surveys and underwent blood draws and nasal swab collections at each visit.RESULTSOur cohort (n = 617) ranged from asymptomatic to critical COVID-19 infections. In total, 40% of participants reported at least 1 symptom associated with COVID-19 six months after diagnosis. Median time from diagnosis to first resolution of all symptoms was 44 days; median time from diagnosis to sustained symptom resolution with no recurring symptoms for 1 month or longer was 214 days. Anti-nucleocapsid IgG level in the first week after positive RT-PCR test and history of lung disease were associated with time to sustained symptom resolution. COVID-19 disease severity, ethnicity, age, sex, and remdesivir use did not affect time to sustained symptom resolution.CONCLUSIONWe found that all disease severities had a similar risk of developing post–COVID-19 syndrome in an ethnically diverse population. Comorbid lung disease and lower levels of initial IgG response to SARS-CoV-2 nucleocapsid antigen were associated with longer symptom duration.TRIAL REGISTRATIONClinicalTrials.gov, NCT04373148.FUNDINGNIH UL1TR003142 CTSA grant, NIH U54CA260517 grant, NIEHS R21 ES03304901, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative, Sunshine Foundation, Crown Foundation, and Parker Foundation.

Severe asthma and personalized approach in the choice of biologic.

Severe asthma requires intensive pharmacological treatment to achieve disease control. Oral corticosteroids are effective, but their use is burdened with important side effects. Biologics targeting the specific inflammatory pathways underpinning the disease have been shown to be effective but not all patients respond equally well. As we treat more patients than those who can respond, our inability to predict responders has important healthcare costs considering that biologics are expensive drugs. Thus, a more precise choice of the 'right patients' to be prescribed with the 'right biologics' would be desirable. Machine learning techniques showed that it is possible to increase our ability to predict outcomes in patients treated with biologics. Recently, we identified by cluster analysis four different clusters within the T2 high phenotype with differential benralizumab response. Two of these clusters, characterized by higher levels of inflammatory markers, showed the highest response rate (80-90%). Machine learning holds promise for asthma research enabling us to predict which patients will respond to which drug. These techniques can facilitate the diagnostic workflow and increase the chance of selecting the more appropriate treatment option for the individual patient, enhancing patient care and satisfaction.

Omics technologies in allergy and asthma research: An EAACI position paper.

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Sex Dimorphism of Allergen-Induced Secreted Proteins in Murine and Human Lungs

Biological sex influences disease severity, prevalence and response to therapy in allergic asthma. However, allergen-mediated sex-specific changes in lung protein biomarkers remain undefined. Here, we report sex-related differences in specific proteins secreted in the lungs of both mice and humans, in response to inhaled allergens. Female and male BALB/c mice (7-8 weeks) were intranasally challenged with the allergen house dust mite (HDM) for 2 weeks. Bronchoalveolar lavage fluid (BALF) was collected 24 hour after the last HDM challenge from allergen-naïve and HDM-challenged mice (N=10 per group, each sex). In a human study, adult participants were exposed to nebulized (2 min) allergens (based on individual sensitivity), BALF was obtained after 24 hour (N=5 each female and male). The BALF samples were examined in immunoblots for the abundance of 10 proteins shown to increase in response to allergen in both murine and human BALF, selected from proteomics studies. We showed significant sex-bias in allergen-driven increase in five out of the 10 selected proteins. Of these, increase in eosinophil peroxidase (EPX) was significantly higher in females compared to males, in both mice and human BALF. We also showed specific sex-related differences between murine and human samples. For example, allergen-driven increase in S100A8 and S100A9 was significantly higher in BALF of females compared to males in mice, but significantly higher in males compared to females in humans. Overall, this study provides sex-specific protein biomarkers that are enhanced in response to allergen in murine and human lungs, informing and motivating translational research in allergic asthma.

Advancement of natural language programming, machine learning and electronic health records for the digital health science field

The widespread use of “electronic health record systems (EHRs)” in health care provides a large amount of real-world data, opening up new opportunities for medical trials. “Deep learning, a subset of machine learning (ML)”, has experienced a meteoric rise in popularity over the last six years, owing to advances in computing power and the accessibility of enormous new datasets. “Natural language processing (NLP)” approaches have been used as an “artificial intelligence strategy” to obtain information from medical narratives in EHRs, as a huge quantity of useful clinical knowledge is contained in clinical stories. This NLP capacity may enable “automated chart review in clinical care to identify individuals with distinct clinical features” and decrease methodological heterogeneity in establishing “phenotypes, masking biological heterogeneity in allergy, asthma, and immunology research”. Aim of this research paper is to understand the advanced technologies such as “Machine Learning, Natural Language Programming” are helpful for digital health field. In this context, secondary data collection method is used to gather information related to this topic.

The Impact of Insulin Resistance on Loss of Lung Function and Response to Treatment in Asthma.

Rationale: The role of obesity-associated insulin resistance (IR) in airflow limitation in asthma is uncertain. Objectives: Using data in the Severe Asthma Research Program 3 (SARP-3), we evaluated relationships between homeostatic measure of IR (HOMA-IR), lung function (cross-sectional and longitudinal analyses), and treatment responses to bronchodilators and corticosteroids. Methods: HOMA-IR values were categorized as without (<3.0), moderate (3.0-5.0), or severe (>5.0). Lung function included FEV1 and FVC measured before and after treatment with inhaled albuterol and intramuscular triamcinolone acetonide and yearly for 5 years. Measurements and Main Results: Among 307 participants in SARP-3, 170 (55%) were obese and 140 (46%) had IR. Compared with patients without IR, those with IR had significantly lower values for FEV1 and FVC, and these lower values were not attributable to obesity effects. Compared with patients without IR, those with IR had lower FEV1 responses to β-adrenergic agonists and systemic corticosteroids. The annualized decline in FEV1 was significantly greater in patients with moderate IR (-41 ml/year) and severe IR (-32 ml/year,) than in patients without IR (-13 ml/year, P < 0.001 for both comparisons). Conclusions: IR is common in asthma and is associated with lower lung function, accelerated loss of lung function, and suboptimal lung function responses to bronchodilator and corticosteroid treatments. Clinical trials in patients with asthma and IR are needed to determine if improving IR might also improve lung function.