To what extent blood granulocyte patterns may predict asthma control remains under-studied. Our aim was to study associations between blood neutrophilia and eosinophilia and asthma control outcomes in adults.Analyses were conducted in 474 asthmatics from the first follow-up of the Epidemiological Study on the Genetics and Environment of Asthma (EGEA2), including 242 asthmatics who were adults a decade earlier (EGEA1). At EGEA2, asthma control was assessed using the Global Initiative for Asthma definition (2015), and asthma exacerbations by use of urgent care or courses of oral corticosteroids in the past year. Blood EOS/EOS was defined as ≥250 eosinophils·mm, respectively, and NEU/NEU as ≥5000 neutrophils·mm, respectively. Estimates were adjusted for age, sex and smoking.At EGEA2, NEU was associated with asthma exacerbations and poor asthma control (OR >2.10). EOS was associated with higher bronchial hyperresponsiveness (BHR) (OR (95% CI) 2.21 (1.24-3.97)), poor lung function (p=0.02) and higher total IgE level (p=0.002). Almost 50% of asthmatics had a persistent pattern between surveys. Persistent NEU was associated with poor asthma control at EGEA2 (OR (95% CI) 3.09 (1.18-7.05)). EOS at EGEA1 and persistent EOS were associated with higher BHR (OR (95% CI) 2.36 (1.10-5.07) and 3.85 (1.11-13.34), respectively), poor lung function (p<0.06) and higher immunoglobulin E level (p<10) at EGEA2.Granulocyte patterns were differently associated with asthma outcomes, suggesting specific roles for each one, which could be tested as predictive signatures.
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http://dx.doi.org/10.1183/13993003.00336-2016 | DOI Listing |
BMC Public Health
January 2025
Department of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Chronic respiratory diseases (CRD) represents a series of lung disorders and is posing a global health burden. Systemic inflammation and phenotypic ageing have been respectively reported to associate with certain CRD. However, little is known about the co-exposures and mutual associations of inflammation and ageing with CRD.
View Article and Find Full Text PDFNPJ Prim Care Respir Med
January 2025
Erasmus MC, Department of General Practice, Rotterdam, The Netherlands.
Asthma and allergic rhinitis (AR) are common disorders of the respiratory tract that often coincide. Control of AR symptoms can improve asthma outcomes in patients with co-existing diseases. Our aim is to produce a systematic review of the effectiveness of conventional anti-AR medication for asthma outcomes in patients with both diseases.
View Article and Find Full Text PDFAm J Transl Res
December 2024
Department of Respiratory Medicine, Hanzhong People's Hospital Hanzhong 723000, Shaanxi, China.
Objective: To investigate the diagnostic value of immunoglobulin E (IgE), fractional of exhaled nitric oxide (FeNO), and peripheral blood eosinophils (EOS) in adult bronchial asthma and to analyze their relationship with asthma severity.
Methods: A retrospective analysis was conducted on 336 patients diagnosed with bronchial asthma and admitted to Xi'an Fourth Hospital from January 2022 to January 2024, forming the asthma group. Additionally, another 127 healthy subjects were selected as the non-asthmatic control group.
Immunol Invest
January 2025
Department of Respiratory Medicine, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China.
Introduction: T helper 17 (Th17) cells have a significant effect in the pathogenesis of asthma, and signal transducer and activator of transcription 3 (STAT3) pathway activation is critical for Th17 cell differentiation. Timosaponin A-III (TA3) was reported to inhibit the STAT3 pathway. Here, we investigated whether TA3 improved asthma by inhibiting the STAT3 pathway.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
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