Michael E. Hyland

Type 2 diabetes is an error in the glycemic control system caused by insulin resistance. Self-organizing control systems use artificial intelligence to monitor and correct how they function and are used in modern applications of robotics. The development of type 2 diabetes is gradual and reversible through lifestyle changes, and this is consistent with insulin resistance being formed through a self-organizing or intelligent control system rather than the traditional control system on which current biomedical control systems are modelled. Lifestyles that induce high levels of glycemic variation would, in an intelligent control system, be detected as excessive oscillation, and an intelligent system can reduce oscillation by reducing the amplification. Insulin resistance represents low amplification in the glycemic control system. Intelligent control system functioning requires a network architecture, so if the glycemic control system is self-organizing, then it should be part of a network system that provides intelligent functioning for other control systems, and error in the glycemic control system would contribute to errors in other control systems and to a state of general network dysregulation. Intelligent systems theory explains how the comorbidities of type 2 diabetes and obesity would form through the dysregulation of a network of metabolic, immune, endocrine, and neurological pathways. The theory predicts that medical and lifestyle interventions for type 2 diabetes could improve comorbidities, but that comorbidities would reduce the effectiveness of those interventions. Studies are proposed to test this new theory that provides a non-blaming narrative for patients and could form a prequel to patient education.

Biologic therapies have transformed care for people with severe asthma, yet treatment response has traditionally been assessed using clinician-derived measures, including lung function, exacerbation rates, and oral corticosteroid use. These metrics, while clinically important, frequently fail to capture what matters most to patients: their ability to participate in daily life, maintain relationships, and sustain work and social roles. The publication of the Core Outcome Measures Set for Severe Asthma (COMSA) and the subsequent CompOsite iNdexes For Response in asthMa (CONFiRM) composite score mark a shift towards patient-centred measurement of treatment response in severe asthma. Rheumatoid arthritis (RA) and inflammatory bowel disease (IBD) travelled this path decades earlier. Both conditions adopted composite scoring tools, such as the Disease Activity Score-28 (DAS28) and the Mayo Score, that incorporated Patient-Reported Outcome Measures (PROMs) alongside objective markers. Initiatives like Outcome Measures in Rheumatology (OMERACT) in RA and Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) in IBD demonstrate that sustained, multidisciplinary collaboration between patients, clinicians, regulators, and industry can successfully embed PROMs into clinical trials and routine care. We argue that timely integration of health-related quality of life measures into clinical trials and practice requires electronic data collection, regulatory endorsement of PROMs as co-primary endpoints, and genuine patient partnership in research, beyond tokenism. By learning from parallel specialties, the asthma community can build a model of care that reflects what truly matters to those living with the disease.

Explanations and treatment of fibromyalgia syndrome (FMS) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are controversial, and outcomes are poor. This paper describes sensation-suppression theory, a theory modelled on self-organizing control systems that are capable of adaptation in response to inputs and used in applications of artificial intelligence. The theory shows how the need to suppress inflammatory and other causes of pain or fatigue due to challenging circumstances sensitizes the neurological processing of pain and fatigue, thereby creating the amplified sensations and abnormal cognitions of central sensitivity syndromes. These syndromes are caused by errors in an evolutionarily early behavior-control mechanism of animals that comes into conflict with the later cognitive behavior-control mechanism of humans. Unlike the cognitive and current biological theories, the sensation-suppression theory explains both the personality and biological risk factors for central sensitivity syndromes and why onset is sometimes gradual and sometimes sudden. A specific form of autonomic dysregulation that could act as a new empirical test of the theory is suggested. Recovery is achieved by reversing the biological homeostatic dysregulation through a specific form of pacing where the person changes from one short, non-stressful activity to another, and where activity is calibrated to the level of illness and the patient’s current biological state. Recovery is hampered or prevented by systemic inflammation and lifestyle obligations. The theory provides a sympathetic narrative for the cause and treatment of FMS and ME/CFS and promotes a recovery lifestyle that prioritizes the needs of the patient. Prevention requires hearing what the body is saying.

The objective was to compare the symptom networks of long‐COVID and chronic fatigue syndrome (CFS) in conjunction with other theoretically relevant diagnoses in order to provide insight into the etiology of medically unexplained symptoms (MUS). This was a cross‐sectional comparison of questionnaire items between six groups identified by clinical diagnosis. All participants completed a 65‐item psychological and somatic symptom questionnaire (GSQ065). Diagnostically labelled groups were long‐COVID (N = 107), CFS (N = 254), irritable bowel syndrome (IBS, N = 369), fibromyalgia (N = 1,127), severe asthma (N = 100) and healthy group (N = 207). The 22 symptoms that best discriminated between the six groups were selected for network analysis. Connectivity, fragmentation and number of symptom clusters (statistically related symptoms) were assessed. Compared to long‐COVID, the symptom networks of CFS, IBS and fibromyalgia had significantly lower connectivity, greater fragmentation and more symptom clusters. The number of clusters varied between 9 for CFS and 3 for severe asthma, and the content of clusters varied across all groups. Of the 33 symptom clusters identified over the six groups 30 clusters were unique. Although the symptom networks of long‐COVID and CFS differ, the variation of cluster content across the six groups is inconsistent with a modular causal structure but consistent with a connectionist (network, parallel distributed processing) biological basis of MUS. A connectionist structure would explain why symptoms overlap and merge between different functional somatic syndromes, the failure to discover a biological diagnostic test and how psychological and behavioral interventions are therapeutic.

Review provided by Michael E. Hyland Roles: Retired; honorary professor at University of Plymouth and emeritus professor at Plymouth Marjon University. mhyland@plymouth.ac.uk The paper ‘Network Neuroscience and Translational Medicine for Understan...

Background: Research into the effects of asthma treatments on the extra-pulmonary symptoms of severe asthma is limited by the absence of a suitable questionnaire. The aim was to create a questionnaire suitable for intervention studies by selecting symptoms that are statistically associated with asthma pathology and therefore may improve when pathology is reduced. Methods: Patients attending a specialist asthma clinic completed the 65-item General Symptom Questionnaire (GSQ-65), a questionnaire validated for assessing symptoms of people with multiple medically unexplained symptoms. Lung function (FEV1%) and cumulative oral corticosteroids (OCS) calculated from maintenance dose plus exacerbations were obtained from clinic records. Pathology was represented by the two components of a principal component analysis (PCA) of FEV1% and OCS. LASSO regression was used to select symptoms that had high coefficients with these two principal components and occurred frequently in severe asthma. Results: 100 patients provided data. PCA revealed two components, one where FEV1% and OCS were inversely related and another where they were directly related. LASSO regression revealed 39 symptoms with non-zero coefficients on one or more of the two principal components from which 16 symptoms were selected for the GSQ-A on the basis of magnitude of coefficient and frequency. Asthma symptoms measured by asthma control questionnaires were excluded. The GSQ-A correlated 0.33 and − 0.34 (p = 0.001) with the two principal components. Conclusion: The GSQ-A assesses the frequency of 16 heterogenous non-respiratory symptoms that are associated with asthma severity using the statistical combination of FEV1% and OCS.

Background The sensitivity of patient-reported outcomes (PROs) to detect the effects of treatment change depends on the match between the change in items of the PRO and the change that takes place in a sample of people. The aim of this study is to compare the sensitivity of different PROs in detecting changes following the initiation of biologic treatment in asthma. Methods: Patients starting a biologic treatment as part of clinical care completed the Asthma Control Questionnaire (ACQ-6), the Severe Asthma Questionnaire (SAQ and SAQ-global scores) and the EQ5D (EQ-5D-5L and EQ5D-VAS) at baseline. They completed the ACQ-6, SAQ, SAQ-global and a retrospective global rating of change (GRoC) scale at weeks 4, 8 and 16 and completed the EQ-5D-5L and EQ5D-VAS at week 16. The SAQ-global and EQ5D-VAS differ but both are single item 100-point questions. Sensitivity was measured by Cohen’s D effect size at each of the three time points. Results: 110 patients were recruited. Depending on the time of assessment, effect size varied between 0.45 and 0.64 for the SAQ, between 0.50 and 0.77 for the SAQ-global; between 0.45 and 0.69 for ACQ-6; between 0.91 and 1.22 for GRoC; 0.32 for EQ-5D-5L and 0.49 for EQ5D-VAS. Conclusion: The sensitivity to change of a questionnaire varies with the time of measurement. The three asthma-specific prospective measures (SAQ, SAQ-global and ACQ-6) have similar sensitivity to change. The single-item EQ5D-VAS was less sensitive than the asthma specific measures and less sensitive than the single-item SAQ-global. The EQ-5D-5L was least sensitive.

Background: The Severe Asthma Questionnaire (SAQ) is a health related quality of life (HRQoL) questionnaire validated for use in severe asthma. It is scored using the mean value of 16 items (SAQ score) in addition to a single item global rating of HRQoL (SAQ-global). The aim was to validate clinically relevant subscales using exploratory factor analysis (EFA). Methods: The SAQ was completed, along with measures of asthma control and EQ5D-5L by patients attending six UK severe asthma centres. Clinical data were included in the analysis. EFA using principal axis factoring and oblimin rotation was used to achieve simple structure of data. Results: 460 patients with severe asthma participated, 65% women, mean age 51 (16–83) years. A three factor solution achieved best fit and showed that the SAQ items formed three distinct but inter-correlated groups of items where items were grouped in a way that was consistent with item content. The three subscales were differentially associated with clinically relevant variables (lung function and mood). Males and females interpreted the question of night disturbance in different ways. Conclusions: This paper provides a template for best practice in the use of EFA when validating HRQoL subscales. The SAQ can be scored as three subscales with content reflecting three different constructs people with severe asthma use when making judgements about their lives. The subscale ‘My Life’ assesses the impact of severe asthma on different life activities, ‘My Mind’ assesses the perceived emotional impact and ‘My Body’ the impact of extra-pulmonary symptoms and side effects.

BackgroundThe Severe Asthma Questionnaire (SAQ) is a health related quality of life (HRQoL) questionnaire validated for use in severe asthma. It is scored using the mean value of 16 items (SAQ score) in addition to a single item global rating of HRQoL (SAQ-global). The aim was to validate clinically relevant subscales using exploratory factor analysis (EFA).MethodsThe SAQ was completed, along with measures of asthma control and EQ5D-5L by patients attending six UK severe asthma centres. Clinical data were included in the analysis. EFA using principal axis factoring and oblimin rotation was used to achieve simple structure of data. Results460 patients with severe asthma participated, 65% women, mean age 51 (16-83) yrs. A three factor solution achieved best fit and showed that the SAQ items formed three distinct but inter-correlated groups of items where items were grouped in a way that was consistent with item content. The three subscales were differentially associated with clinically relevant variables (lung function and mood). Males and females interpreted the question of night disturbance in different ways.ConclusionsThis paper provides a template for best practice in the use of EFA when validating HRQoL subscales. The SAQ can be scored as three subscales with content reflecting three different constructs people with severe asthma use when making judgements about their lives. The subscale ‘My Life’ assesses the impact of severe asthma on different life activities, ‘My Mind’ assesses the perceived emotional impact and ‘My Body’ the impact of extra-pulmonary symptoms and side effects.