Li Yu

Research question: Cold exposure commonly worsens chronic cough, but mechanisms linking peripheral cold sensing to central cough hypersensitivity remain unclear. We examined whether cutaneous transient receptor potential ankyrin 1 (TRPA1) signalling contributes to cold-aggravated cough hypersensitivity via synaptic sensitisation and substance p (SP)/neurokinin-1 (NK-1) signalling in the paraventricular nucleus (PVN) and nucleus tractus solitarius (NTS). Materials and methods: Cough hypersensitivity was induced in conscious guinea pigs by repeated citric acid inhalation. Trunk–limb cooling was applied with head and airways at room temperature. Cough reactivity to inhaled cinnamaldehyde was assessed. We inhibited cutaneous TRPA1 with HC-030031 and blocked NTS NK-1 receptors via stereotaxic microinjection. Neuronal activation, SP/NK-1 and airway TRPA1 expression, BALF SP levels, and NTS synaptic transmission were examined. Results: Cold exposure increased cough reactivity in citric acid-sensitised guinea pigs and upregulated c-fos, SP, and NK-1 in PVN and NTS. Cutaneous HC-030031 pretreatment attenuated cold-induced neuronal and neuropeptide activation. NTS NK-1 receptor blockade reduced cold-aggravated cough responses, lowered BALF SP and airway TRPA1 upregulation, and dampened NTS central sensitisation. Patch-clamp recordings showed enhanced excitatory synaptic drive to NTS neurons after cold exposure, reversed by NK-1 antagonism; action potential firing was unchanged. Answer to the question: Cold exposure is associated with aggravated cough hypersensitivity and altered cutaneous TRPA1 signalling, PVN activation, and SP/NK-1-mediated synaptic transmission in the NTS. Our data point to a peripheral-to-central pathway whereby cutaneous cold sensing may modulate brainstem cough processing. Cutaneous TRPA1 and NTS NK-1 signalling are candidate targets for further investigation in cold-aggravated cough.

Background: Asthma is a common respiratory disease with a high disease burden in China. The patient population with severe asthma remains largely unexplored. Objectives: To describe the disease burden, treatment pattern and eosinophilic phenotype of Chinese patients with moderate-to-severe asthma. Design: This is a retrospective baseline analysis of the real-world, observational, multicenter PRESENT study (NCT06422663). Methods: Patients receiving Global Initiative for Asthma Step 3–5 treatment were categorized into protocol-specified severe asthma (uncontrolled asthma with medium-dose inhaled corticosteroids [ICS], or asthma with high-dose ICS) and moderate asthma subgroups. This baseline analysis reported patient characteristics, disease burden, treatment patterns, eosinophilic phenotype and testing rates of biomarkers at enrolment. Disease burden was assessed by historical asthma exacerbation rate within the past year, Asthma Control Test (ACT) score and lung function. Results: Of the 498 patients included, 203 (40.8%) were identified as having protocol-specified severe asthma. Notable disease burden was observed, as reflected by the high proportion of patients experiencing asthma exacerbation (severe asthma: 35.0%; moderate asthma: 14.6%), low ACT score (mean [SD]: severe asthma: 17.4 [3.8]; moderate asthma: 20.6 [3.7]) and declined pre-bronchodilator forced expiratory volume in one second (pre-BD FEV ₁ ; median [interquartile range]: severe asthma: 2.0 [1.5, 2.5]; moderate asthma: 2.2 [1.7, 2.8]). Overall, testing rates of blood eosinophil (EOS) were 25.3% (in the past seven days), 49.0% (in the past three months) and 68.9% (in the past one year). Among patients who tested EOS once and twice in the past one year, 62.5% (180/288) and 76.4% (42/55) had blood EOS counts ≥150 cells/μL, respectively. Testing rates of different biomarkers varied but were generally low. Conclusion: Notable disease burden and sub-optimal management were observed in Chinese patients with moderate-to-severe asthma. Biomarker-based phenotyping of severe asthma was unsatisfactory. Treatment escalation and routine monitoring of blood EOS level might be considered. Registration: ClinicalTrials.gov , NCT06422663, registered on March 19, 2024, https://clinicaltrials.gov/study/NCT06422663 .

Chronic cough is defined as a cough lasting no less than 8 weeks, and 10%–42% of patients with chronic cough retain their cough after comprehensive examination and treatment. The terms idiopathic chronic cough (ICC), unexplained chronic cough (UCC), and refractory chronic cough (RCC) are frequently used to describe this clinical condition. However, these terms are often used interchangeably in articles, leading to heterogeneity in the populations included in the studies and compromising the value of the findings in guiding clinical practice. This article aims to clarify the current terminological confusion by reviewing the definitions and relationships among ICC, UCC, and RCC in the literature, and to discuss the emerging role of cough hypersensitivity syndrome (CHS) as an important pathophysiological framework for understanding chronic cough, with a focus on its clinical value, current challenges, and future perspectives.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-responsive cough (CRC) accounts for the majority of the causes of chronic cough, and the diagnostic process is relatively complicated. To develop a predictive model for diagnosis of CRC, which is based on readily available clinical information. A retrospective dataset of 304 cases was used for training, and a prospective dataset of 131 cases was used for temporal single-center validation. Candidate predictors were screened via univariate analysis and refined using LASSO regression. A final multivariable model was constructed through stepwise logistic regression and presented as a clinical nomogram. The model's performance was rigorously evaluated in terms of discrimination (AUC-ROC), calibration (calibration curve), and clinical utility (decision curve analysis). The robustness of the model predictors was further supported by their consistency with the key variables identified by an optimal machine learning model. Of 20 candidate variables based on patient basic information and test results, 7 variables were selected as optimal predictors to establish a CRC prediction model for chronic cough, including reflux symptoms, history of allergic diseases, blood eosinophils, IgE, CRP, FEV₁/FVC, and FeNO₅₀. The model demonstrated good discrimination and calibration in both the training set (AUC = 0.900 [0.865,0.935], MAE = 0.017) and the validation set (AUC = 0.840 [0.771,0.909], MAE = 0.046). The results of decision curve analysis revealed that using our models to predict CRC in chronic cough would add more benefit than either the treat-all scheme or the treat-none scheme. The variable selection for the nomogram was further validated by the high degree of agreement with the feature importance rankings derived from the top-performing machine learning model (Random Forest). Our study has developed a nomogram model, along with a simplified version, based on easily accessible clinical information and pulmonary function tests. The model demonstrates strong predictive performance and good calibration, providing clinicians with a rapid aid for initiating targeted treatment.

Background: Cough variant asthma (CVA) is a common cause of chronic cough but remains underdiagnosed due to limited access to bronchial provocation test. Objective: To develop a clinical prediction model for CVA based on more accessible indicators. Design: A single-center retrospective cohort study. Methods: A retrospective cohort of patients with chronic cough from January 2024 to December 2024 was included. The patients were randomly divided into a training set and an internal validation set at a ratio of 7:3. Univariable and multivariable logistic regression analyses were used to identify independent predictors of CVA. A nomogram prediction model was constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: A total of 323 patients with chronic cough were included, with 226 assigned to the training set (45 with CVA, 181 with non-CVA) and 97 to the internal validation set (23 with CVA, 74 with non-CVA). Multivariable logistic regression analysis identified increased eosinophils in induced sputum, elevated peripheral blood eosinophil count (PBEC), raised FeNO ₅₀ , and reduced maximal mid-expiratory flow (MMEF) as independent predictors of CVA (all p < 0.05). The nomogram model developed based on these indicators demonstrated strong discriminative ability, with area under the ROC curve (AUC) of 0.93 in the training set and 0.92 in the internal validation set. Calibration curves indicated favorable predictive performance of the model. DCA confirmed net clinical benefit. Conclusion: The CVA clinical prediction model based on induced sputum cytology, PBEC, FeNO ₅₀ and MMEF shows excellent discrimination, accuracy, calibration, and clinical applicability.

Background/Objectives: Gastroesophageal reflux-induced chronic cough (GERC) is a common cause of chronic cough, and a substantial proportion of patients show an inadequate response to standard anti-reflux therapy. Psychological comorbidities, including somatic symptom disorder (SSD), have been increasingly recognized in this population. However, the prevalence and clinical characteristics of SSD in patients with GERC remain unclear. This study aimed to determine the prevalence of SSD in patients with GERC and to characterize the associated clinical and psychological features. Methods: In this prospective observational study, consecutive patients with GERC diagnosed at Tongji Hospital, Shanghai, China, between January 2024 and June 2025 were consecutively enrolled. SSD was assessed using a structured diagnostic interview based on DSM-5 criteria. Patients were categorized into SSD+ and SSD− groups. Clinical and psychological characteristics were compared between groups, and binary logistic regression was performed to identify factors associated with SSD. Results: A total of 215 patients with GERC were included, of whom 22.3% (48/215) were diagnosed with SSD. Compared with the SSD− group, patients with SSD had higher healthcare utilization, poorer response to standard anti-reflux therapy, increased cough sensitivity, poorer cough-related quality of life, and greater psychological distress. Multivariate logistic regression identified increased cough sensitivity and higher anxiety and depression scores as independent factors associated with SSD. Conclusions: SSD is relatively common in patients with GERC and is associated with cough hypersensitivity, poorer response to anti-reflux therapy, and greater psychological burden. These findings suggest that SSD may represent a clinically relevant phenotype of GERC, and patients with coexisting SSD may require more comprehensive psychosomatic assessment and multidisciplinary management.

This study was aimed at exploring the prevalence of somatic symptom disorder (SSD) in patients with chronic cough and comparing SSD comorbid (SSD+) and non

Background: The diagnosis of cough-variant asthma (CVA) traditionally relies on clinical manifestations and bronchial provocation test (BPT). However, the BPT can be costly and complex, and poses risks of severe bronchospasm. Most CVA patients exhibit type 2 airway inflammation and small airway dysfunction. Identifying practical biomarkers such as total immunoglobulin E (tIgE), percentage of eosinophil cell count in induced sputum (sEOS%), peripheral blood eosinophil count (PBEC), and fractional exhaled nitric oxide (FeNO) may enhance the diagnostic process for cough-variant asthma. Objective: This study aims to evaluate and compare the diagnostic utility of T2 inflammatory biomarkers combined with small airway parameters for diagnosing CVA. Methods: This was a single-center, cross-sectional diagnostic study including 211 patients with chronic cough. Participants underwent spirometry, FeNO measurement, and assessment of eosinophilic inflammation, including sEOS%, PBEC, and tIgE. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of individual parameters and combined models. Results: CVA was diagnosed in 27.5% of patients. Compared with non-CVA patients, those with CVA showed significantly higher levels of T2 inflammatory biomarkers and reduced small airway function. Among individual biomarkers, FeNO demonstrated the highest diagnostic accuracy (AUC = 0.79), followed by sEOS% (AUC = 0.75), tIgE (AUC = 0.71), and PBEC (AUC = 0.66). Among small airway parameters, FEF_50% showed the best performance (AUC = 0.79). Combined models significantly improved diagnostic accuracy, with FEF_50% combined with FeNO or sEOS%, achieving the highest AUC (0.82). Correlation analysis revealed that higher inflammatory biomarker levels were associated with reduced small airway function. Conclusion: The combination of T2 inflammatory biomarkers and small airway function parameters improves the diagnostic accuracy for CVA. This approach may provide a practical and non-invasive strategy for identifying CVA, particularly in settings where bronchial provocation testing is not readily available.