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The incidence of obesity and asthma continues to enhance, significantly impacting global public health. Adipose tissue is an organ that secretes hormones and cytokines, causes meta-inflammation, and contributes to the intensification of bronchial hyperreactivity, oxidative stress, and consequently affects the different phenotypes of asthma in obese people. As body weight increases, the risk of severe asthma increases, as well as more frequent exacerbations requiring the use of glucocorticoids and hospitalization, which consequently leads to a deterioration of the quality of life. This review discusses the relationship between obesity and severe asthma, the underlying molecular mechanisms, changes in respiratory function tests in obese people, its impact on the occurrence of comorbidities, and consequently, a different response to conventional asthma treatment. The article also reviews research on possible future therapies for severe asthma. The manuscript is a narrative review of clinical trials in severe asthma and comorbid obesity. The articles were found in the PubMed database using the keywords asthma and obesity. Studies on severe asthma were then selected for inclusion in the article. The sections: 'The classification connected with asthma and obesity', 'Obesity-related changes in pulmonary functional tests', and 'Obesity and inflammation', include studies on subjects without asthma or non-severe asthma, which, according to the authors, familiarize the reader with the pathophysiology of obesity-related asthma.

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BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes. OBJECTIVES: To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data. METHODS: We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross-validated F1 score to support their distinctiveness. RESULTS: Data from 13,498 patients with asthma with available postbronchodilator spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young nonsevere asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest body mass index (mean ± SD, 44.44 ± 7.83 kg/m2), and the highest proportion of females (77.5%) and Blacks (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower percent of predicted FEV1 of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest percent of predicted FEV1 of 68.08 (15.02), the highest postbronchodilator reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/µL) (34.8%). CONCLUSIONS: Using real-world data and unsupervised ML, we classified asthma into 5 clinically important subphenotypes where group-specific asthma treatment and management strategies can be designed and deployed.

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Background: Uncontrolled asthma (UCA) is different from severe asthma and can be identified in children across all ranges of prescribed treatment. Objective: Our aim was to characterize uncontrolled childhood asthma in pediatric specialist care. Methods: We performed a nationwide cross-sectional study of 5497 children (aged 6-17 years) with asthma who were treated by pediatricians at outpatient clinics during 2019 and registered in the Swedish National Airway Register. UCA was defined as an Asthma Control Test score of 19 or lower and/or 2 or more exacerbations in the past year and/or an FEV1 value less than 80% predicted. Treatment was categorized from step 1 to step 5 according to the Global Initiative for Asthma. Results: UCA was identified in 1690 children (31%), of whom 64% had an Asthma Control Test score of 19 or lower, 20% had recurrent exacerbations, and 31% had an FEV1 value less than 80% predicted. UCA was associated with female sex (odds ratio [OR] = 1.29 [95% CI = 1.15-1.45]), older age (OR = 1.02 [95% CI = 1.00-1.04]), obesity (OR = 1.43 [95% CI = 1.12-1.83]), and more treatment using steps 1 and 2 as a reference (step 3, OR = 1.28 [95% CI = 1.12-1.46]); steps 4-5, OR = 1.32 [95% CI = 1.10-1.57]). UCA in children prescribed treatment steps 1 and 2 (group UCA1-2) occurred in 28% of all children at this treatment step (n = 887). Children in group UCA1-2 had exacerbations more frequently than did those children with UCA who were prescribed steps 4 and 5 treatment (24% vs 15% [P = .001]). Conclusion: UCA was common and associated with female sex, increasing age, obesity, and higher Global Initiative for Asthma treatment step. Surprisingly, UCA was also common in children prescribed less than the maximum treatment, and those children could be considered undertreated patients.

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Interleukin (IL)-36 family is closely associated with inflammation and consists of IL-36α, IL-36ß, IL-36γ, and IL-36Ra. The role of IL-36 in the context of asthma and asthmatic phenotypes is not well characterized. We examined the sputum IL-36 levels in patients with different asthma phenotypes in order to unravel the mechanism of IL-36 in different asthma phenotypes. Our objective was to investigate the induced sputum IL-36α, IL-36ß, IL-36γ, and IL-36Ra concentrations in patients with mild asthma, and to analyze the relationship of these markers with lung function and other cytokines in patients with different asthma phenotypes. Induced sputum samples were collected from patients with mild controlled asthma (n = 62, 27 males, age 54.77 ± 15.49) and healthy non-asthmatic controls (n = 16, 10 males, age 54.25 ± 14.60). Inflammatory cell counts in sputum were determined. The concentrations of IL-36 and other cytokines in the sputum supernatant were measured by ELISA and Cytometric Bead Array. This is the first study to report the differential expression of different isoforms of IL-36 in different asthma phenotypes. IL-36α and IL-36ß concentrations were significantly higher in the asthma group (P = 0.003 and 0.031), while IL-36Ra concentrations were significantly lower (P < 0.001) compared to healthy non-asthmatic controls. Sputum IL-36α and IL-36ß concentrations in the neutrophilic asthma group were significantly higher than those in paucigranulocytic asthma (n = 24) and eosinophilic asthma groups (n = 23). IL-36α and IL-36ß showed positive correlation with sputum neutrophils and total cell count (R = 0.689, P < 0.01; R = 0.304, P = 0.008; R = 0.689, P < 0.042; R = 0.253, P = 0.026). In conclusion, IL-36α and IL-36ß may contribute to asthma airway inflammation by promoting neutrophil recruitment in airways. Our study provides insights into the inflammatory pathways of neutrophilic asthma and identifies potential therapeutic target.

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Asthma occurs frequently as a comorbid condition in many patients presenting with common otolaryngology conditions, such as allergic rhinitis and chronic sinusitis with nasal polyps. The classic presentation of asthma includes symptoms of wheezing, shortness of breath, and chest tightness but can include other symptoms such as cough. The diagnosis is made mainly through history, although pulmonary function testing, spirometry, fractional exhaled nitric oxide, and impulse oscillometry may also prove helpful.

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Severe asthma stands as a formidable contributor to both mortality and morbidity of patients suffering asthma, casting substantial social and economic shadows on communities. As our understanding of asthma's pathophysiology deepens, a beacon of hope emerges in the form of biological targeted therapies, offering a promising avenue for the management of this challenging condition. These therapies, by precisely inhibiting or modulating pivotal molecules in the inflammatory cascade, offer potential benefits in symptom alleviation, lung function enhancement, and risk reduction of acute exacerbations. They signify a paradigm shift in severe asthma treatment. Within the confines of this article, we embark on a systematic exploration of the immunological underpinnings that define severe asthma. By delving into the intricacies of the immune system's role in exacerbating this condition, we aim to offer a comprehensive assessment of both the current landscape and the future prospects of biological therapies. Our objective is to provide a scientifically robust and valuable reference that can guide the individualized treatment of patients grappling with severe asthma.

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Purpose: Asthma is one of the most common chronic diseases affecting 10%-30% of children in Saudi Arabia. Although data exist on adult asthma phenotyping and endotyping in Saudi Arabia, little is known about asthma phenotypes in Saudi children. Patients and Methods: This cross-sectional study enrolled pediatric patients diagnosed with bronchial asthma and followed in the pediatric pulmonology clinic of the Abha Maternity and Children Hospital between August 2021 and May 2023. Results: A total of 321 children (aged 5-14 years) were analyzed. The population was classified into allergic [169 (52.6%)], eosinophilic [144 (44.9%)], and overlapping allergic and eosinophilic asthma [97 (30.2%)] phenotypes. Regarding asthma severity, 35.5%, 50.2%, and 14.3% were classified as mild, moderate, and severe, respectively. Of the 321 patients in the study, 124 (38.6%) had at least one asthma exacerbation that required hospitalization. The number of reported missed school days in the previous year was 1571 days [190 (59.2%) patients reported at least one missed school day]. The factors associated with the likelihood of uncontrolled asthma for all study participants included: emergency room (ER) visit last year (OR = 3.7, 95% CI:0.6-15.9]), overlapping eosinophilic and allergic (OR = 3.2, 95% CI = 1.8-5.9), and allergic phenotype (OR = 2.7, 95% CI = 1.3-5.4). The level of asthma control differed significantly among the three asthma phenotypes (p = 0.037). Conclusion: Allergic asthma is the most prevalent asthma phenotype in this study, followed by the eosinophilic phenotype. The research has also shown that several factors predict uncontrolled asthma, including a family history of asthma, previous admission to the PICU, and previous hospitalization ever. There is, therefore, a definite need for multicenter cohort studies to better understand the phenotypes and endotypes of childhood asthma, as it could offer therapeutic and prognostic relevance.

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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.

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RATIONALE AND OBJECTIVE: Plasma extracellular vesicles (EVs) represent a vital source of molecular information about health and disease states. Due to their heterogenous cellular sources, EVs and their cargo may predict specific pathomechanisms behind disease phenotypes. Here we aimed to utilize EV microRNA (miRNA) signatures to gain new insights into underlying molecular mechanisms of obesity-associated low type-2 asthma. METHODS: Obese low type-2 asthma (OA) and non-obese low type-2 asthma (NOA) patients were selected from an asthma cohort conjointly with healthy controls. Plasma EVs were isolated and characterised by nanoparticle tracking analysis. EV-associated small RNAs were extracted, sequenced and bioinformatically analysed. RESULTS: Based on EV miRNA expression profiles, a clear distinction between the three study groups could be established using a principal component analysis. Integrative pathway analysis of potential target genes of the differentially expressed miRNAs revealed inflammatory cytokines (e.g., interleukin-6, transforming growth factor-beta, interferons) and metabolic factors (e.g., insulin, leptin) signalling pathways to be specifically associated with OA. The miR-17-92 and miR-106a-363 clusters were significantly enriched only in OA. These miRNA clusters exhibited discrete bivariate correlations with several key laboratory (e.g., C-reactive protein) and lung function parameters. Plasma EV miRNA signatures mirrored blood-derived CD4+ T-cell transcriptome data, but achieved an even higher sensitivity in identifying specifically affected biological pathways. CONCLUSION: The identified plasma EV miRNA signatures and particularly the miR-17-92 and -106a-363 clusters were capable to disentangle specific mechanisms of the obesity-associated low type-2 asthma phenotype, which may serve as basis for stratified treatment development.

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Clarifying inflammatory processes and categorising asthma into phenotypes and endotypes improves asthma management. Obesity worsens severe asthma and reduces quality of life, although its specific molecular impact remains unclear. We previously demonstrated that hsa-miR-26a-1-3p and hsa-miR-376a-3p, biomarkers related to an inflammatory profile, discriminate eosinophilic from non-eosinophilic asthmatics. We aimed to study hsa-miR-26a-1-3p, hsa-miR-376a-3p, and their target genes in asthmatic subjects with or without obesity to find biomarkers and comprehend obese asthma mechanisms. Lung tissue samples were obtained from asthmatic patients (n = 16) and healthy subjects (n = 20). We measured miRNA expression using RT-qPCR and protein levels (IGF axis) by ELISA in confirmation samples from eosinophilic (n = 38) and non-eosinophilic (n = 39) obese (n = 26) and non-obese (n = 51) asthma patients. Asthmatic lungs showed higher hsa-miR-26a-1-3p and hsa-miR-376a-3p expression than healthy lungs. A study of seven genes regulated by these miRNAs revealed differential expression of IGFBP3 between asthma patients and healthy individuals. In obese asthma patients, we found higher hsa-miR-26a-1-3p and IGF-1R values and lower values for hsa-miR-376a-3p and IGFBP-3. Hsa-miR-26a-1-3p and IGFBP-3 were directly and inversely correlated with body mass index, respectively. Hsa-miR-26a-1-3p and hsa-miR-376a-3p could be used as biomarkers to phenotype patients with eosinophilic and non-eosinophilic asthma in relation to comorbid obesity.

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BACKGROUND: Asthma is a chronic inflammatory disorder of the airways with diverse overlapping pathologies and phenotypes contributing to a significant heterogeneity in clinical manifestations. Obesity may modify asthma risk, phenotype, and prognosis. A suggested mechanism linking obesity and asthma is through systemic inflammation. Adipokines secreted by adipose tissue were suggested to provide a link between obesity and asthma. OBJECTIVE: To have an understanding for the contribution of adiponectin, resistin and MCP-1 to development of distinct asthma phenotype in overweight/obese children through assessment of their serum level and correlation to pulmonary function tests. SUBJECTS AND METHODS: The study included 29 normal weight asthmatics, 23 overweight/obese asthmatic children and 30 controls. All cases were subjected to detailed history taking, thorough examination and pulmonary function tests. Serum adiponectin, resistin, MCP-1 and IgE were assessed to all recruited subjects. RESULTS: Adiponectin level was significantly higher in overweight/obese asthmatics (24900 ± 1600 ng/ml) compared to normal weight asthmatics (21700 ± 1700 ng/ml) and control (23000 ± 3200 ng/ml), (p < 0.001 & 0.051 respectively). Normal weight asthmatics had significantly lower adiponectin level than control, (p = 0.039). A significant low level of MCP-1 in overweight/obese asthmatics (149.5 (20-545) ng/L) compared to control (175 (28 -1123.5) ng/L), p = 0.037. No significant difference was found regarding resistin. Normal weight asthmatics had significantly lower FEV1% and FVC% compared to overweight/obese asthmatics (p = 0.036, 0.016 respectively). A significant positive correlation was found between (FEV1%, FVC) and BMI in normal weight asthmatics (P = 0.01, < 0.01 respectively) and a significant negative correlation between PEF and BMI (-0.42, p = 0.05) in obese/overweight asthmatics. Resistin/adiponectin ratio was not affected by sex, degree of asthma severity or level of asthma control in either normal weight or overweight/obese asthmatic. CONCLUSION: This work could suggest that adiponectin may play a role in overweight/obese asthma phenotype where it is possible to have a dual action (pro & anti- inflammatory). It seems that resistin had no role in asthma pathogenesis.

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Rationale: Asthma is a heterogeneous condition, and longitudinal phenotyping may provide new insights into the origins and outcomes of the disease. Objectives: We aimed to characterize the longitudinal phenotypes of asthma between the first and sixth decades of life in a population-based cohort study. Methods: Respiratory questionnaires were collected at seven time points in the TAHS (Tasmanian Longitudinal Health Study) when participants were aged 7, 13, 18, 32, 43, 50, and 53 years. Current-asthma and ever-asthma status was determined at each time point, and group-based trajectory modeling was used to characterize distinct longitudinal phenotypes. Linear and logistic regression models were fitted to investigate associations of the longitudinal phenotypes with childhood factors and adult outcomes. Measurements and Main Results: Of 8,583 original participants, 1,506 had reported ever asthma. Five longitudinal asthma phenotypes were identified: early-onset adolescent-remitting (40%), early-onset adult-remitting (11%), early-onset persistent (9%), late-onset remitting (13%), and late-onset persistent (27%). All phenotypes were associated with chronic obstructive pulmonary disease at age 53 years, except for late-onset remitting asthma (odds ratios: early-onset adolescent-remitting, 2.00 [95% confidence interval (CI), 1.13-3.56]; early-onset adult-remitting, 3.61 [95% CI, 1.30-10.02]; early-onset persistent, 8.73 [95% CI, 4.10-18.55]; and late-onset persistent, 6.69 [95% CI, 3.81-11.73]). Late-onset persistent asthma was associated with the greatest comorbidity at age 53 years, with increased risk of mental health disorders and cardiovascular risk factors. Conclusions: Five longitudinal asthma phenotypes were identified between the first and sixth decades of life, including two novel remitting phenotypes. We found differential effects of these phenotypes on risk of chronic obstructive pulmonary disease and nonrespiratory comorbidities in middle age.

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Introduction: The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. Methods: We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. Results: Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. Conclusion: We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps. (AU)

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INTRODUCTION: The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. METHODS: We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. RESULTS: Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. CONCLUSION: We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.

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Summary: Background. Asthma is a disease that combines different biological mechanisms, inflammatory pathways, and phenotypic features. Our aim was to investigate the demographic and disease characteristics of patients with asthma and to reveal the distribution with different phenotypes according to endotype groups. Methods. Patients were identified as eosinophilic if the absolute eosinophil count was measured at least once ≥ 300/µL during the oral corticosteroid free period or ≥ 150/µL under oral corticosteroids. Patients sensitive to at least one inhalant allergen with skin prick test and/ or spIgE measurement were defined as allergic. They were categorized into four main endotypes. Results. Data of 405 asthma patients with a median age of 50.9 years were analyzed. The prominent clinical and phenotypic characteristics of the study group were being obese (43.2%) or overweight (32%), severe asthma (49.6%), adult-onset (56.1%) or late-onset asthma (35.3%). The distribution of the four main endotypes according to eosinophilic and/or allergic status, is as follows: 22.7% allergic-eosinophilic (AE), 27.9% nonallergic-eosinophilic (NAE), 22.9% allergic-noneosinophilic (ANE), 26.4% nonallergic-noneosinophilic (NANE). While most severe asthma patients were in the AE and NAE groups, those with early-onset asthma were in AE and ANE, and those with late-onset asthma were in the NAE and NANE groups. The proportion of uncontrolled patients was higher in the NAE group. Among the severe asthma patients, the rate of uncontrolled disease was higher in those with NANE asthma. Conclusions. Different phenotypes were more closely related to some endotypes. This may allow the clinicians to identify patients and predict appropriate treatment modalities and response for individualized care.

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Asthma is a common, complex heterogeneous disease often beginning in early life and is characterized by reversible airflow obstruction. The phenotypic differences that exist in children with asthma may impact underlying comorbid conditions and pharmacologic treatment choices. Prenatal factors for increased risk of asthma could include maternal diet and the maternal microbiome. Evidence also suggests that postnatal microbial exposures and colonization contribute to the risk of allergic diseases and asthma. After confirming the diagnosis, asthma management in children centers on 3 broad areas: pharmacologic treatment, treatment of underlying comorbidities, and education of the patient and caregivers on the importance of adherence and device technique. Moreover, social determinants of health significantly impact on symptom burden and treatment response.

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OBJECTIVE: Sufficient vitamin D (25-hydroxyvitamin D [25(OH)D]) serum levels are associated with decreased asthma symptoms. Our aim was to investigate associations between vitamin D and atopy, asthma, asthma severity, and asthma phenotypes in Brazilian teenagers. METHODS: This cross-sectional study involved 942 individuals (11-19 years old) engaged in an asthma cohort. The ISAAC questionnaire was employed to diagnosis asthma and asthma severity. Serum allergen-specific immunoglobulin E (sIgE) was measured by ImmunoCap and serum 25(OH)D was measured by ELISA. We calculated the correlation between sIgE and 25(OH)D. We used multivariate logistic regression analysis to assess associations of interest. RESULTS: We found that 25(OH)D deficiency was positively associated with atopy (OR 1.45, confidence interval [CI] 1.05-2.00) and high levels of this vitamin negatively correlated with sIgE to Dermatophagoides pteronyssinus (r = -0.11, p = 0.019). The average 25(OH)D serum level was 27.0 ± 9.5 ng/ml; 366 individuals (38.8%) had a sufficient level. There was no association between 25(OH)D and asthma, asthma severity or asthma phenotypes in the population. However, sex was a possible effect modifier of the association between vitamin D and asthma: insufficiency in asthmatic women (86%) was higher than in asthmatic men (42%), and there was an association between insufficient vitamin D levels and greater asthma risk only in women (OR = 3.06, 95% CI 1.16-8.07). CONCLUSION: We have shown that vitamin D deficiency was associated with greater risk of atopy in both sexes and vitamin D insufficiency was associated with asthma only in women. There was no association between vitamin D levels and asthma phenotypes or asthma severity.

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BACKGROUND: Dyslipidemia has been widely documented to be associated with cardiovascular disease, and recent studies have found an association with asthma prevalence. However, longitudinal studies investigating the relationships between dyslipidemia, asthma phenotypes, and future asthma exacerbations (AEs) are lacking. OBJECTIVE: To investigate the relationships between dyslipidemia, asthma phenotypes, and AEs. METHODS: This study used an observational cohort study design with a 12-month follow-up. All subjects underwent serum lipid measurement, and they were then classified into 2 groups: the normal-lipidemia group and the dyslipidemia group. Demographic and clinical information and details regarding pulmonary function and asthma phenotypes at baseline were collected. All patients were followed up regularly to assess AEs. Associations of dyslipidemia with airway obstruction and asthma phenotypes were assessed at baseline, whereas dyslipidemia and AEs were assessed longitudinally. RESULTS: A total of 477 patients with asthma were consecutively enrolled in this study. At baseline, the dyslipidemia group (n = 218) had a higher proportion of uncontrolled asthma, defined by the 6-item Asthma Control Questionnaire score (≥1.5). Furthermore, dyslipidemia was associated with severe asthma, nonallergic asthma, asthma with fixed airflow limitation, and older adult asthma phenotypes at baseline. In addition, dyslipidemia was associated with increased frequencies of severe AEs and moderate to severe AEs during the 12-month follow-up. In sensitivity analyses, after excluding the patients who were receiving statins, results did not differ significantly from those of the main analysis. CONCLUSIONS: We identified the clinical relevance of dyslipidemia, which is associated with specific asthma phenotypes and increased AEs, independent of other components of metabolic syndrome. These findings highlight the importance of considering dyslipidemia as an "extrapulmonary trait" in asthma management.

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BACKGROUND: The differences in molecular mechanisms during a stable period and the changes in the inflammatory responses during exacerbations between distinct severe asthma phenotypes remain unclear. In this study, we aimed to characterize stable and exacerbation period serum cytokine and periostin levels of 5 different predefined severe asthma phenotypes with real-life data. Changes in the viral infection-induced exacerbations were also analyzed. METHODS: Serum levels of 8 cytokines and periostin were measured from the sera obtained from the adult patients with five different severe asthma phenotypes based on the presence/absence of aeroallergen sensitivity, peripheral eosinophilia and chronic rhinosinusitis with nasal polyposis (CRSwNP) during stable and exacerbation periods, and from the matched controls. RESULTS: Serum IL-13, IL-25, TSLP, and periostin levels were similar between the patient and the control groups during stable and exacerbation periods. Serum IL-25 and TSLP levels, and peripheral eosinophil count and periostin level showed a strong correlation. Stable period periostin levels were significantly higher in eosinophilic patients, and eosinophilic patients without long-term systemic steroid therapy had higher IL-13 levels. Compared to stable period, exacerbation period serum periostin levels found significantly lower [5853 (2309-8427) pg/mL vs. 4479 (2766-6495) pg/mL; p = 0.05] and periostin levels were much lower in viral infection-induced exacerbations [2913 (893-4770) pg/mL vs. 7094 (4782-9596) pg/mL; p = 0.022]. DISCUSSION: Our study showed that serum periostin levels were decreased in viral infection-induced exacerbations and increased in the presence of eosinophilia independent from atopy and it can help to differentiate eosinophilia even if the patient is under long-term systemic steroid therapy. Also, serum IL-13 levels may reflect peripheral eosinophilia in patients without long-term systemic steroid use.

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