A circadian based inflammatory response – implications for respiratory disease and treatment

Circadian clocks regulate the daily timing of many of our physiological, metabolic and biochemical functions. The immune system also displays circadian oscillations in immune cell count, synthesis and cytokine release, clock gene expression in cells and organs of the immune system as well as clock-controlled genes that regulate immune function. Circadian disruption leads to dysregulation of immune responses and inflammation which can further disrupt circadian rhythms. The response of organisms to immune challenges, such as allergic reactions also vary depending on time of the day, which can lead to detrimental responses particularly during the rest and early active periods. This review evaluates what is currently known in terms of circadian biology of immune response and the cross-talk between circadian and immune system. We discuss the circadian pattern of three respiratory-related inflammatory diseases, chronic obstructive pulmonary disease, allergic rhinitis and asthma. Increasing our knowledge on circadian patterns of immune responses and developing chronotherapeutic studies in inflammatory diseases with strong circadian patterns will lead to preventive measures as well as improved therapies focussing on the circadian rhythms of symptoms and the daily variation of the patients’ responses to medication

Sleep quality in middle-aged and elderly Chinese: distribution, associated factors and associations with cardio-metabolic risk factors

Background Poor sleep quality has been associated with increased risk of heart disease, diabetes and mortality. However, limited information exists on the distribution and determinants of sleep quality and its associations with cardio-metabolic risk factors in Chinese populations. We aimed to evaluate this in the current study. Methods A cross-sectional survey conducted in 2005 of 1,458 men and 1,831 women aged 50–70 years from urban and rural areas of Beijing and Shanghai. Using a questionnaire, sleep quality was measured in levels of well, common and poor. Comprehensive measures of socio-demographical and health factors and biomarkers of cardio-metabolic disease were recorded. These were evaluated in association with sleep quality using logistic regression models. Results Half of the population reported good sleep quality. After adjusting for potential confounders, women and Beijing residents had almost half the probability to report good sleep quality. Good physical and mental health (good levels of self-rated health (OR 2.48; 95%CI 2.08 to 2.96) and no depression (OR 4.05; 95%CI 3.12 to 5.26)) related to an increased chance of reporting good sleep quality, whereas short sleep duration (<7 hrs OR 0.10; 95%CI 0.07 to 0.14)) decreased it substantially. There were significant associations between levels of sleep quality and concentrations of plasma insulin, total and LDL cholesterol, and index of insulin resistance. Conclusion Levels of good sleep quality in middle-age and elderly Chinese were low. Gender, geographical location, self-rated health, depression and sleep quantity were major factors associated with sleep quality. Prospective studies are required to distil the factors that determine sleep quality and the effects that sleep patterns exert on cardio-metabolic health

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Clinical side effects of continuous positive airway pressure in patients with obstructive sleep apnoea.

CPAP is considered the gold standard treatment in OSA and is highly efficacious in controlling OSA symptoms. However, treatment effectiveness is limited because of many factors including low adherence due to side effects. This review highlights the range of side effects associated with CPAP therapy in patients with OSA. This information is important for the initiation of patients onto CPAP as well as their continued care while on treatment, given the increase in non-medically supervised CPAP care models in use globally

Drug effects on ventilatory control and upper airway physiology related to sleep apnea

Understanding the inter-relationship between pharmacological agents, ventilatory control, upper airway physiology and their consequent effects on sleep-disordered breathing may provide new directions for targeted drug therapy. Where available, this review focuses on human studies that contain both drug effects on sleep-disordered breathing and measures of ventilatory control or upper airway physiology. Many of the existing studies are limited in sample size or comprehensive methodology. At times, the presence of paradoxical findings highlights the complexity of drug therapy for OSA. The existing studies also highlight the importance of considering inter-individual pharmacokinetics and underlying causes of sleep apnea in interpreting drug effects on sleep-disordered breathing. Practical ways to assess an individual's ventilatory control and how it interacts with upper airway physiology is required for future targeted pharmacotherapy in sleep apnea. © 2013 Elsevier B.V

Integrating psychology and medicine in CPAP adherence – New concepts?

To date, continuous positive airway pressure (CPAP) is the most effective intervention in the treatment of obstructive sleep apnoea, but adherence to this treatment is often less than optimal. A variety of factors and interventions that influence and improve CPAP use have been examined. There is increasing recognition of the multifaceted nature of CPAP adherence: the patient's psychological profile and social environment have been recognised, in addition to the more extensively researched patient's treatment and physiological profile. Understanding how these multiple factors impact on CPAP use in an integrative fashion might provide us with a useful holistic model of CPAP adherence. This concept of integration--a biopsychosocial (BPS) approach to health and illness--has previously been described to understand care provision for various chronic health disorders. This paper proposes an adherence framework, whereby variables integrally affect CPAP use. The BPS model has been considered for nearly 35 years; the presence of poor CPAP adherence was acknowledged in the early 1990s--it is timely to incorporate this approach into our care pathway of CPAP users

Increased Adherence to CPAP With a Group Cognitive Behavioral Treatment Intervention: A Randomized Trial

Study Objective: To improve adherence to continuous positive airway pressure (CPAP) treatment in participants with obstructive sleep apnea (OSA) using a cognitive behavioral therapy (CBT) intervention. Design: A randomized controlled trial. Setting: A major teaching hospital in Sydney (2005). Participants: One hundred individuals (96 men), ranging in age from 32 to 81 years, diagnosed with OSA. Intervention: Two 1-hour CBT interventions (including a video of real CPAP users) plus treatment as usual (mask fitting and information) or treatment as usual only. Measurements and Results: Hours of CPAP usage was assessed at 7 nights and 28 nights. Adherence was defined as usage at least 4 hours per night. Questionnaires measuring self-efficacy, social support, and expectancy (mediators of adherence) were given after intervention or after usual treatment. A higher adherence to CPAP therapy was found in the CBT group (2.9 hours difference) relative to treatment as usual (