Co-design of a digital dietary intervention for adults at risk of type 2 diabetes

Background Co-design has the potential to create interventions that lead to sustainable health behaviour change. Evidence suggests application of co-design in various health domains has been growing; however, few public-facing digital interventions have been co-designed to specifically address the needs of adults at risk of Type 2 diabetes (T2D). This study aims to: (1) co-design, with key stakeholders, a digital dietary intervention to promote health behaviour change among adults at risk of T2D, and (2) evaluate the co-design process involved in developing the intervention prototype. Methods The co-design study was based on a partnership between nutrition researchers and designers experienced in co-design for health. Potential end-users (patients and health professionals) were recruited from an earlier stage of the study. Three online workshops were conducted to develop and review prototypes of an app for people at risk of T2D. Themes were inductively defined and aligned with persuasive design (PD) principles used to inform ideal app features and characteristics. Results Participants were predominantly female (range 58–100%), aged 38 to 63 years (median age = 59 years), consisting of a total of 20 end-users and four experts. Participants expressed the need for information from credible sources and to provide effective strategies to overcome social and environmental influences on eating behaviours. Preferred app features included tailoring to the individual’s unique characteristics, ability to track and monitor dietary behaviour, and tools to facilitate controlled social connectivity. Relevant persuasive design principles included social support, reduction (reducing effort needed to reach target behaviour), tunnelling (guiding users through a process that leads to target behaviour), praise, rewards, and self-monitoring. The most preferred prototype was the Choices concept, which focusses on the users’ journey of health behaviour change and recognises progress, successes, and failures in a supportive and encouraging manner. The workshops were rated successful, and feedback was positive. Conclusions The study’s co-design methods were successful in developing a functionally appealing and relevant digital health promotion intervention. Continuous engagement with stakeholders such as designers and end-users is needed to further develop a working prototype for testing

Identifying critical features of type two diabetes prevention interventions: A Delphi study with key stakeholders

Aims This study aims to identify critically important features of digital type two diabetes mellitus (T2DM) prevention interventions. Methods A stakeholder mapping exercise was undertaken to identify key end-user and professional stakeholders, followed by a three-round Delphi procedure to generate and evaluate evidence statements related to the critical elements of digital T2DM prevention interventions in terms of product (intervention), price (funding models/financial cost), place (distribution/delivery channels), and promotion (target audiences). Results End-user (n = 38) and professional (n = 38) stakeholders including patients, dietitians, credentialed diabetes educators, nurses, medical doctors, research scientists, and exercise physiologists participated in the Delphi study. Fifty-two critical intervention characteristics were identified. Future interventions should address diet, physical activity, mental health (e.g. stress, diabetes-related distress), and functional health literacy, while advancing behaviour change support. Programs should be delivered digitally or used multiple delivery modes, target a range of population subgroups including children, and be based on collaborative efforts between national and local and government and non-government funded organisations. Conclusions Our findings highlight strong support for digital health to address T2DM in Australia and identify future directions for T2DM prevention interventions. The study also demonstrates the feasibility and value of stakeholder-led intervention development processes

Effects of weight loss on a low-carbohydrate diet on flow-mediated dilatation, adhesion molecules and adiponectin

Our aim was to determine whether short-term weight loss on a low-carbohydrate/low-saturated fat diet improved endothelial function compared with a conventional high-carbohydrate diet, as this diet is expected to lower both blood glucose and LDL-cholesterol. In a randomised parallel design of two energy-restricted diets in an outpatient setting, thirty-six subjects (BMI 33 (sem 4) kg/m2) were randomised to a low- or high-carbohydrate diet both low in saturated fat. Flow-mediated dilatation (FMD), fasting glucose, insulin, lipids, adiponectin and adhesion molecules were measured at baseline, during weight loss and at 52 weeks. FMD did not change with either diet (5·2 (sem 0·6) to 5·5 (sem 0·6) %) despite weight loss of 5 % and significant reductions in glucose and insulin and LDL-cholesterol and was not different after sustained weight loss of 5 % at 52 weeks. Adiponectin fell by 6 % at 12 weeks (P = 0·1) with weight loss but rose by 17 % at 12 months (P < 0·05) with 5 % weight loss. There were no effects of diet. In contradistinction, adhesion molecules fell at 12 weeks, vascular cell adhesion molecule-1 by 14 % and intracellular adhesion molecule-1 by 13 % (both P < 0·05). There were correlations between change in adiponectin at 12 months and change in HDL (r 0·778, P < 0·01) and glucose (r − 0·563, P = 0·057). In summary, weight loss does not improve FMD. Novel cardiovascular risk factors improved at 12 weeks but the improvement in adiponectin was delayed.Jennifer B. Keogh, Grant D. Brinkworth and Peter M. Clifto

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Gene expression variability across cells and species shapes innate immunity.

As the first line of defence against pathogens, cells mount an innate immune response, which varies widely from cell to cell. The response must be potent but carefully controlled to avoid self-damage. How these constraints have shaped the evolution of innate immunity remains poorly understood. Here we characterize the innate immune response's transcriptional divergence between species and variability in expression among cells. Using bulk and single-cell transcriptomics in fibroblasts and mononuclear phagocytes from different species, challenged with immune stimuli, we map the architecture of the innate immune response. Transcriptionally diverging genes, including those that encode cytokines and chemokines, vary across cells and have distinct promoter structures. Conversely, genes that are involved in the regulation of this response, such as those that encode transcription factors and kinases, are conserved between species and display low cell-to-cell variability in expression. We suggest that this expression pattern, which is observed across species and conditions, has evolved as a mechanism for fine-tuned regulation to achieve an effective but balanced response

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease