A common model for the breathlessness experience across cardiorespiratory disease

Association of computed tomography-derived pectoralis muscle area and density with disease severity and respiratory symptoms in patients with chronic obstructive pulmonary disease: A case-control study

RATIONALE AND OBJECTIVES

Computed tomography (CT) is commonly used and offers an additional viewpoint for evaluating extrapulmonary symptoms, disease severity, and muscle atrophy. This study assessed whether the pectoralis muscle area (PMA) and pectoralis muscle density (PMD) are lower in patients with chronic obstructive pulmonary disease (COPD) than in healthy controls and elucidated their relationships with these variables.

MATERIALS AND METHODS

The participants were enrolled in the hospital outpatient clinic between October 2023 and May 2024. Information was obtained from questionnaires, lung function, and CT imaging findings. On full-inspiratory CT, the PMA and PMD were measured at the aortic arch level using predetermined attenuation ranges of -29 and 150 Hounsfield units. We observed lower PMA and PMD and evaluated their associations with lung function, respiratory symptoms, and CT imaging findings in patients with COPD.

RESULTS

Overall, 120 participants were enrolled at baseline (60 healthy controls and 60 patients with COPD). PMA and PMD were lower with progressive airflow limitation severity in those with COPD. The degree of emphysema and air trapping, as well as lung function, were correlated with PMA and PMD (P < 0.05), although not with the COPD Assessment Test or modified Medical Research Council scores (P > 0.05).

CONCLUSION

Participants with COPD had smaller PMA and PMD. These measurements were correlated with the severity of airflow limitation, lung function, emphysema, and air trapping, suggesting that these features of the pectoralis muscle obtained from CT are helpful in assessments of patients with COPD.

Breathlessness in a virtual world: An experimental paradigm testing how discrepancy between VR visual gradients and pedal resistance during stationary cycling affects breathlessness perception

INTRODUCTION

The sensation of breathlessness is often attributed to perturbations in cardio-pulmonary physiology, leading to changes in afferent signals. New evidence suggests that these signals are interpreted in the light of prior "expectations". A misalignment between afferent signals and expectations may underly unexplained breathlessness. Using a novel immersive virtual reality (VR) exercise paradigm, we investigated whether manipulating an individual's expectation of effort (determined by a virtual hill gradient) may alter their perception of breathlessness, independent from actual effort (the physical effort of cycling).

METHODS

Nineteen healthy volunteers completed a single experimental session where they exercised on a cycle ergometer while wearing a VR headset. We created an immersive virtual cycle ride where participants climbed up 100 m hills with virtual gradients of 4%, 6%, 8%, 10% and 12%. Each virtual hill gradient was completed twice: once with a 4% cycling ergometer resistance and once with a 6% resistance, allowing us to dissociate expected effort (virtual hill gradient) from actual effort (power). At the end of each hill, participants reported their perceived breathlessness. Linear mixed effects models were used to examine the independent contribution of actual effort and expected effort to ratings of breathlessness (0-10 scale).

RESULTS

Expectation of effort (effect estimate ± std. error, 0.63 ± 0.11, P < 0.001) and actual effort (0.81 ± 0.21, P < 0.001) independently explained subjective ratings of breathlessness, with comparable contributions of 19% and 18%, respectively. Additionally, we found that effort expectation accounted for 6% of participants' power and was a significant, independent predictor (0.09 ± 0.03; P = 0.001).

CONCLUSIONS

An individuals' expectation of effort is equally important for forming perceptions of breathlessness as the actual effort required to cycle. A new VR paradigm enables this to be experimentally studied and could be used to re-align breathlessness and enhance training programmes.

Nonpharmacological management of psychological distress in people with COPD

Psychological distress is prevalent in people with COPD and relates to a worse course of disease. It often remains unrecognised and untreated, intensifying the burden on patients, carers and healthcare systems. Nonpharmacological management strategies have been suggested as important elements to manage psychological distress in COPD. Therefore, this review presents instruments for detecting psychological distress in COPD and provides an overview of available nonpharmacological management strategies together with available scientific evidence for their presumed benefits in COPD. Several instruments are available for detecting psychological distress in COPD, including simple questions, questionnaires and clinical diagnostic interviews, but their implementation in clinical practice is limited and heterogeneous. Moreover, various nonpharmacological management options are available for COPD, ranging from specific cognitive behavioural therapy (CBT) to multi-component pulmonary rehabilitation (PR) programmes. These interventions vary substantially in their specific content, intensity and duration across studies. Similarly, available evidence regarding their efficacy varies significantly, with the strongest evidence currently for CBT or PR. Further randomised controlled trials are needed with larger, culturally diverse samples and long-term follow-ups. Moreover, effective nonpharmacological interventions should be implemented more in the clinical routine. Respective barriers for patients, caregivers, clinicians, healthcare systems and research need to be overcome.

Breathlessness in COPD: linking symptom clusters with brain activity

Background

Current models of breathlessness often fail to explain disparities between patients' experiences of breathlessness and objective measures of lung function. While a mechanistic understanding of this discordance has thus far remained elusive, factors such as mood, attention and expectation have all been implicated as important modulators of breathlessness. Therefore, we have developed a model to better understand the relationships between these factors using unsupervised machine learning techniques. Subsequently we examined how expectation-related brain activity differed between these symptom-defined clusters of participants.

Methods

A cohort of 91 participants with mild-to-moderate chronic obstructive pulmonary disease (COPD) underwent functional brain imaging, self-report questionnaires and clinical measures of respiratory function. Unsupervised machine learning techniques of exploratory factor analysis and hierarchical cluster modelling were used to model brain–behaviour–breathlessness links.

Results

We successfully stratified participants across four key factors corresponding to mood, symptom burden and two capability measures. Two key groups resulted from this stratification, corresponding to high and low symptom burden. Compared with the high symptom burden group, the low symptom burden group demonstrated significantly greater brain activity within the anterior insula, a key region thought to be involved in monitoring internal bodily sensations (interoception).

Conclusions

This is the largest functional neuroimaging study of COPD to date, and is the first to provide a clear model linking brain, behaviour and breathlessness expectation. Furthermore, it was possible to stratify participants into groups, which then revealed differences in brain activity patterns. Together, these findings highlight the value of multimodal models of breathlessness in identifying behavioural phenotypes and for advancing understanding of differences in breathlessness burden.

Towards individualised treatments for chronic breathlessness with functional neuroimaging: revealing the factors underlying the breathlessness experience in COPDhttps://bit.ly/3a8fXPt