Global rates of obesity and type 2 diabetes mellitus (T2DM) are increasing globally concomitant with a rising prevalence of sleep deprivation and sleep disorders. Understanding the links between ...sleep, obesity and T2DM might offer an opportunity to develop better prevention and treatment strategies for these epidemics. Experimental studies have shown that sleep restriction is associated with changes in energy homeostasis, insulin resistance and β-cell function. Epidemiological cohort studies established short sleep duration as a risk factor for developing obesity and T2DM. In addition, small studies suggested that short sleep duration was associated with less weight loss following lifestyle interventions or bariatric surgery. In this article, we review the epidemiological evidence linking sleep duration to obesity and T2DM and plausible mechanisms. In addition, we review the impact of changes in sleep duration on obesity and T2DM.
Globally, there are now over 160 million confirmed cases of COVID-19 and more than 3 million deaths. While the majority of infected individuals recover, a significant proportion continue to ...experience symptoms and complications after their acute illness. Patients with ‘long COVID’ experience a wide range of physical and mental/psychological symptoms. Pooled prevalence data showed the 10 most prevalent reported symptoms were fatigue, shortness of breath, muscle pain, joint pain, headache, cough, chest pain, altered smell, altered taste and diarrhoea. Other common symptoms were cognitive impairment, memory loss, anxiety and sleep disorders. Beyond symptoms and complications, people with long COVID often reported impaired quality of life, mental health and employment issues. These individuals may require multidisciplinary care involving the long-term monitoring of symptoms, to identify potential complications, physical rehabilitation, mental health and social services support. Resilient healthcare systems are needed to ensure efficient and effective responses to future health challenges.
Previous studies have been unclear about the cardiovascular risks for metabolically healthy obese individuals.
This study examined the associations among metabolically healthy obese individuals and 4 ...different presentations of incident cardiovascular disease in a contemporary population.
We used linked electronic health records (1995 to 2015) in The Health Improvement Network (THIN) to assemble a cohort of 3.5 million individuals, 18 years of age or older and initially free of cardiovascular disease. We created body size phenotypes defined by body mass index categories (underweight, normal weight, overweight, and obesity) and 3 metabolic abnormalities (diabetes, hypertension, and hyperlipidemia). The primary endpoints were the first record of 1 of 4 cardiovascular presentations (coronary heart disease CHD, cerebrovascular disease, heart failure, and peripheral vascular disease).
During a mean follow-up of 5.4 years, obese individuals with no metabolic abnormalities had a higher risk of CHD (multivariate-adjusted hazard ratio HR: 1.49; 95% confidence interval CI: 1.45 to 1.54), cerebrovascular disease (HR: 1.07; 95% CI: 1.04 to 1.11), and heart failure (HR: 1.96; 95% CI: 1.86 to 2.06) compared with normal weight individuals with 0 metabolic abnormalities. Risk of CHD, cerebrovascular disease, and heart failure in normal weight, overweight, and obese individuals increased with increasing number of metabolic abnormalities.
Metabolically healthy obese individuals had a higher risk of coronary heart disease, cerebrovascular disease, and heart failure than normal weight metabolically healthy individuals. Even individuals who are normal weight can have metabolic abnormalities and similar risks for cardiovascular disease events.
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Gestational diabetes mellitus (GDM) is associated with developing type 2 diabetes, but very few studies have examined its effect on developing cardiovascular disease.
We conducted a retrospective ...cohort study utilizing a large primary care database in the United Kingdom. From 1 February 1990 to 15 May 2016, 9,118 women diagnosed with GDM were identified and randomly matched with 37,281 control women by age and timing of pregnancy (up to 3 months). Adjusted incidence rate ratios (IRRs) with 95% confidence intervals (CIs) were calculated for cardiovascular risk factors and cardiovascular disease. Women with GDM were more likely to develop type 2 diabetes (IRR = 21.96; 95% CI 18.31-26.34) and hypertension (IRR = 1.85; 95% CI 1.59-2.16) after adjusting for age, Townsend (deprivation) quintile, body mass index, and smoking. For ischemic heart disease (IHD), the IRR was 2.78 (95% CI 1.37-5.66), and for cerebrovascular disease 0.95 (95% CI 0.51-1.77; p-value = 0.87), after adjusting for the above covariates and lipid-lowering medication and hypertension at baseline. Follow-up screening for type 2 diabetes and cardiovascular risk factors was poor. Limitations include potential selective documentation of severe GDM for women in primary care, higher surveillance for outcomes in women diagnosed with GDM than control women, and a short median follow-up postpartum period, with a small number of outcomes for IHD and cerebrovascular disease.
Women diagnosed with GDM were at very high risk of developing type 2 diabetes and had a significantly increased incidence of hypertension and IHD. Identifying this group of women in general practice and targeting cardiovascular risk factors could improve long-term outcomes.
Increased hepatic glucose output, the primary liver dysregulation associated with Type 2 diabetes mellitus (T2DM), is not directly or effectively targeted by the currently available classes of ...glucose-lowering medications except metformin. This unmet need might be addressed through activation of a specific enzyme-member of the hexokinase family, namely glucokinase (GK). GK serves as a "glucose-sensor" or "glucose receptor" in pancreatic cells, eliciting glucose-stimulated insulin secretion, and as glucose "gate-keeper" in hepatocytes, promoting hepatic glucose uptake and glycogen synthesis and storage. GK activation by small molecules present an alternative approach to restore/improve glycaemic control in patients with T2DM. GK activators (GKAs) may increase insulin secretion from the pancreas and promote glycogen synthesis in the liver, and hence reduce hepatic glucose output. Despite several setbacks in their development, interest in the GKA class has been renewed, particularly since the introduction of a novel, dual-acting full GKA, dorzagliatin, and a novel hepatoselective molecule, TTP399. In this article we provide an overview of the role, efficacy, safety and future developments of GKAs in the management of T2DM.
The use of primary care electronic health records for research is abundant. The benefits gained from utilising such records lies in their size, longitudinal data collection and data quality. However, ...the use of such data to undertake high quality epidemiological studies, can lead to significant challenges particularly in dealing with misclassification, variation in coding and the significant effort required to pre-process the data in a meaningful format for statistical analysis. In this paper, we describe a methodology to aid with the extraction and processing of such databases, delivered by a novel software programme; the “Data extraction for epidemiological research” (DExtER). The basis of DExtER relies on principles of extract, transform and load processes. The tool initially provides the ability for the healthcare dataset to be extracted, then transformed in a format whereby data is normalised, converted and reformatted. DExtER has a user interface designed to obtain data extracts specific to each research question and observational study design. There are facilities to input the requirements for; eligible study period, definition of exposed and unexposed groups, outcome measures and important baseline covariates. To date the tool has been utilised and validated in a multitude of settings. There have been over 35 peer-reviewed publications using the tool, and DExtER has been implemented as a validated public health surveillance tool for obtaining accurate statistics on epidemiology of key morbidities. Future direction of this work will be the application of the framework to linked as well as international datasets and the development of standardised methods for conducting electronic pre-processing and extraction from datasets for research purposes.
The association between allergic diseases and autoimmune disorders is not well established. Our objective was to determine incidence rates of autoimmune disorders in allergic rhinitis/conjunctivitis ...(ARC), atopic eczema and asthma, and to investigate for co-occurring patterns.
This was a retrospective cohort study (1990-2018) employing data extracted from The Health Improvement Network (UK primary care database). The exposure group comprised ARC, atopic eczema and asthma (all ages). For each exposed patient, up to two randomly selected age- and sex-matched controls with no documented allergic disease were used. Adjusted incidence rate ratios (aIRRs) were calculated using Poisson regression. A cross-sectional study was also conducted employing Association Rule Mining (ARM) to investigate disease clusters.
782 320, 1 393 570 and 1 049 868 patients with ARC, atopic eczema and asthma, respectively, were included. aIRRs of systemic lupus erythematosus (SLE), Sjögren's syndrome, vitiligo, rheumatoid arthritis, psoriasis, pernicious anaemia, inflammatory bowel disease, coeliac disease and autoimmune thyroiditis were uniformly higher in the three allergic diseases compared with controls. Specifically, aIRRs of SLE (1.45) and Sjögren's syndrome (1.88) were higher in ARC; aIRRs of SLE (1.44), Sjögren's syndrome (1.61) and myasthenia (1.56) were higher in asthma; and aIRRs of SLE (1.86), Sjögren's syndrome (1.48), vitiligo (1.54) and psoriasis (2.41) were higher in atopic eczema. There was no significant effect of the three allergic diseases on multiple sclerosis or of ARC and atopic eczema on myasthenia. Using ARM, allergic diseases clustered with multiple autoimmune disorders. Three age- and sex-related clusters were identified, with a relatively complex pattern in females ≥55 years old.
The long-term risks of autoimmune disorders are significantly higher in patients with allergic diseases. Allergic diseases and autoimmune disorders show age- and sex-related clustering patterns.
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