Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes.

AIMS/HYPOTHESIS: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. RESULTS: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. CONCLUSIONS/INTERPRETATION: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.

AIMS/HYPOTHESIS: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. METHODS: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic. RESULTS: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. CONCLUSIONS/INTERPRETATION: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. DATA AVAILABILITY: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .

Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes.

AIMS/HYPOTHESIS: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA). METHODS: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events. RESULTS: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. CONCLUSIONS/INTERPRETATION: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries.

Small airway fibroblasts from patients with chronic obstructive pulmonary disease exhibit cellular senescence.

Small airway disease (SAD) is a key early-stage pathology of chronic obstructive pulmonary disease (COPD). COPD is associated with cellular senescence whereby cells undergo growth arrest and express the senescence-associated secretory phenotype (SASP) leading to chronic inflammation and tissue remodeling. Parenchymal-derived fibroblasts have been shown to display senescent properties in COPD, however small airway fibroblasts (SAFs) have not been investigated. Therefore, this study investigated the role of these cells in COPD and their potential contribution to SAD. To investigate the senescent and fibrotic phenotype of SAF in COPD, SAFs were isolated from nonsmoker, smoker, and COPD lung resection tissue (n = 9-17 donors). Senescence and fibrotic marker expressions were determined using iCELLigence (proliferation), qPCR, Seahorse assay, and ELISAs. COPD SAFs were further enriched for senescent cells using FACSAria Fusion based on cell size and autofluorescence (10% largest/autofluorescent vs. 10% smallest/nonautofluorescent). The phenotype of the senescence-enriched population was investigated using RNA sequencing and pathway analysis. Markers of senescence were observed in COPD SAFs, including senescence-associated ß-galactosidase, SASP release, and reduced proliferation. Because the pathways driving this phenotype were unclear, we used cell sorting to enrich senescent COPD SAFs. This population displayed increased p21CIP1 and p16INK4a expression and mitochondrial dysfunction. RNA sequencing suggested these senescent cells express genes involved in oxidative stress response, fibrosis, and mitochondrial dysfunction pathways. These data suggest COPD SAFs are senescent and may be associated with fibrotic properties and mitochondrial dysfunction. Further understanding of cellular senescence in SAFs may lead to potential therapies to limit SAD progression.NEW & NOTEWORTHY Fibroblasts and senescence are thought to play key roles in the pathogenesis of small airway disease and COPD; however, the characteristics of small airway-derived fibroblasts are not well explored. In this study we isolate and enrich the senescent small airway-derived fibroblast (SAF) population from COPD lungs and explore the pathways driving this phenotype using bulk RNA-seq.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes.

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

The role of knowledge, primary care and community engagement to improve breast-screening access for Pakistani women in the United Kingdom: A secondary analysis of a qualitative study.

OBJECTIVE: Breast cancer incidence is rising among Pakistani women in the United Kingdom. However, uptake of breast screening remains low. This study aimed to improve access to breast screening for British-Pakistani women by exploring their knowledge of breast cancer and the role of primary care and community networks to support screening access amongst British-Pakistani women. METHODS: We undertook a secondary qualitative analysis of 18 semi-structured interviews with British-Pakistani women from East Lancashire in the United Kingdom. Anonymized transcripts of the interviews were used for a thematic analysis. RESULTS: Three themes were identified in the interviewees' responses: (i) 'Women's knowledge of breasts and breast cancer', which described how a cultural taboo exists around Pakistani women's bodies and around breast cancer; (ii) 'Role of primary care', which detailed how General Practitioners can support informed decisions and offer a trusted and valued information source; (iii) 'Community engagement', which described the potential to disseminate breast-screening information through the whole community, including primary care providers, all family members and mosques. CONCLUSIONS: Our analysis suggested three main targets for future interventions to improve access to breast screening for British-Pakistani women: (i) co-produced strategies to increase knowledge of breasts and breast screening; (ii) greater collaboration with local General Practitioners to support women to make informed choices about screening; and (iii) community engagement involving General Practitioners and community leaders, to inform everyone - not just screening-age women - about breast cancer and screening.

Single cell quantification of microRNA from small numbers of non-invasively sampled primary human cells.

Expression levels of microRNAs (miRNAs) in single cells are low and conventional miRNA detection methods require amplification that can be complex, time-consuming, costly and may bias results. Single cell microfluidic platforms have been developed; however, current approaches are unable to absolutely quantify single miRNA molecules expressed in single cells. Herein, we present an amplification-free sandwich hybridisation assay to detect single miRNA molecules in single cells using a microfluidic platform that optically traps and lyses individual cells. Absolute quantification of miR-21 and miR-34a molecules was achieved at a single cell level in human cell lines and validated using real-time qPCR. The sensitivity of the assay was demonstrated by quantifying single miRNA molecules in nasal epithelial cells and CD3+ T-cells, as well as nasal fluid collected non-invasively from healthy individuals. This platform requires ~50 cells or ~30 µL biofluid and can be extended for other miRNA targets therefore it could monitor miRNA levels in disease progression or clinical studies.

Cardiovascular Safety in Type 2 Diabetes With Sulfonylureas as Second-line Drugs: A Nationwide Population-Based Comparative Safety Study.

OBJECTIVE: To assess the real-world cardiovascular (CV) safety for sulfonylureas (SU), in comparison with dipeptidyl peptidase 4 inhibitors (DPP4i) and thiazolidinediones (TZD), through development of robust methodology for causal inference in a whole nation study. RESEARCH DESIGN AND METHODS: A cohort study was performed including people with type 2 diabetes diagnosed in Scotland before 31 December 2017, who failed to reach HbA1c 48 mmol/mol despite metformin monotherapy and initiated second-line pharmacotherapy (SU/DPP4i/TZD) on or after 1 January 2010. The primary outcome was composite major adverse cardiovascular events (MACE), including hospitalization for myocardial infarction, ischemic stroke, heart failure, and CV death. Secondary outcomes were each individual end point and all-cause death. Multivariable Cox proportional hazards regression and an instrumental variable (IV) approach were used to control confounding in a similar way to the randomization process in a randomized control trial. RESULTS: Comparing SU to non-SU (DPP4i/TZD), the hazard ratio (HR) for MACE was 1.00 (95% CI: 0.91-1.09) from the multivariable Cox regression and 1.02 (0.91-1.13) and 1.03 (0.91-1.16) using two different IVs. For all-cause death, the HR from Cox regression and the two IV analyses was 1.03 (0.94-1.13), 1.04 (0.93-1.17), and 1.03 (0.90-1.17). CONCLUSIONS: Our findings contribute to the understanding that second-line SU for glucose lowering are unlikely to increase CV risk or all-cause mortality. Given their potent efficacy, microvascular benefits, cost effectiveness, and widespread use, this study supports that SU should remain a part of the global diabetes treatment portfolio.

Initiation of glucose-lowering drugs reduces the anticoagulant effect of warfarin-But not through altered drug metabolism in patients with type 2 diabetes.

AIMS: Drug metabolism might be altered in patients with type 2 diabetes. We aimed to evaluate if initiation of glucose-lowering drugs impacts warfarin efficacy and drug metabolism. METHODS: First, we conducted a register-based self-controlled cohort study on Danish and Scottish warfarin users. Warfarin efficacy (international normalized ratio [INR]) was compared before and after initiation of glucose-lowering drugs. Second, we conducted a clinical pharmacokinetic trial comprising treatment-naïve type 2 diabetes patients. Patients ingested probe drugs for drug-metabolizing enzymes (the Basel Cocktail) before initiating glucose-lowering treatment, and after 3 and 12 weeks of treatment. Drug metabolism, glycaemic control, and inflammation were assessed on each visit. RESULTS: In the Danish and Scottish cohorts (n = 982 and n = 44, respectively), initiating glucose-lowering drugs reduced warfarin efficacy. INR decreased from 2.47 to 2.21 in the Danish cohort (mean difference -0.26; 95% CI -0.35; -0.17) and from 2.33 to 2.13 in the Scottish cohort (-0.21; 95% CI -0.52; 0.11) after initiation of glucose-lowering treatment. This impact on INR was more pronounced among individuals with stronger effects of glucose-lowering treatment. In the clinical pharmacokinetic trial (n = 10), initiating metformin did not affect drug metabolism after 3 weeks (geometric mean ratio of CYP3A metabolic ratio: 1.12 [95% CI: 0.95; 1.32]) or 12 weeks of metformin treatment. Glycaemic control improved during treatment, while inflammation remained low and unchanged during treatment. CONCLUSIONS: In conclusion, initiation of glucose-lowering drugs among chronic warfarin users seems associated with a reduction in INR, particularly among individuals with a large decrease in HbA1c . This effect seems unrelated to CYP enzyme activity and warfarin drug metabolism.

IL-36 receptor agonist and antagonist imbalance drives neutrophilic inflammation in COPD.

Current treatments fail to modify the underlying pathophysiology and disease progression of chronic obstructive pulmonary disease (COPD), necessitating alternative therapies. Here, we show that COPD subjects have increased IL-36γ and decreased IL-36 receptor antagonist (IL-36Ra) in bronchoalveolar and nasal fluid compared with control subjects. IL-36γ is derived from small airway epithelial cells (SAEC) and is further induced by a viral mimetic, whereas IL-36Ra is derived from macrophages. IL-36γ stimulates release of the neutrophil chemoattractants CXCL1 and CXCL8, as well as elastolytic matrix metalloproteinases (MMPs) from small airway fibroblasts (SAF). Proteases released from COPD neutrophils cleave and activate IL-36γ, thereby perpetuating IL-36 inflammation. Transfer of culture media from SAEC to SAF stimulated release of CXCL1, which was inhibited by exogenous IL-36Ra. The use of a therapeutic antibody that inhibits binding to the IL-36R attenuated IL-36γ-driven inflammation and cellular crosstalk. We have demonstrated a mechanism for the amplification and propagation of neutrophilic inflammation in COPD and have shown that blocking this cytokine family via a IL-36R neutralizing antibody could be a promising therapeutic strategy in the treatment of COPD.

The feasibility of implementing risk stratification into a national breast cancer screening programme: a focus group study investigating the perspectives of healthcare personnel responsible for delivery.

BACKGROUND: Providing women with personalized estimates of their risk of developing breast cancer, as part of routine breast cancer screening programmes, allows women at higher risk to be offered more frequent screening or drugs to reduce risk. For this to be feasible, the concept and practicalities have to be acceptable to the healthcare professionals who would put it in to practice. The present research investigated the acceptability to healthcare professionals who were responsible for the implementation of this new approach to screening in the ongoing BC-Predict study. METHODS: Four focus groups were conducted with 29 healthcare professionals from a variety of professional backgrounds working within three breast screening services in north-west England. An inductive-manifest thematic analysis was conducted. RESULTS: Overall, healthcare professionals viewed the implementation of personalised breast cancer risk estimation as a positive step, but discussion focused on concerns. Three major themes are presented. (1) Service constraints highlights the limited capacity within current breast services and concerns about the impact of additional workload. (2) Risk communication concerns the optimal way to convey risk to women within resource constraints. (3) Accentuating inequity discusses how risk stratification could decrease screening uptake for underserved groups. CONCLUSIONS: Staff who implemented risk stratification considered it a positive addition to routine screening. They considered it essential to consider improving capacity and demands on healthcare professional time. They highlighted the need for skilled communication of risks and new pathways of care to ensure that stratification could be implemented in financially and time constrained settings without impacting negatively on women.

Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes.

Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from 23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.

Autophagy in asthma and chronic obstructive pulmonary disease.

Autophagy (or macroautophagy) is a key cellular process that removes damaged molecules (particularly proteins) and subcellular organelles to maintain cellular homeostasis. There is growing evidence that abnormalities in autophagy may contribute to the pathogenesis of many chronic diseases, including asthma and chronic obstructive pulmonary disease (COPD). In asthma, increased autophagy plays a role in promoting type 2 immune responses and eosinophilic inflammation, whereas decreased autophagy may be important in neutrophilic asthma. Acute exposure to cigarette smoke may activate autophagy, resulting in ciliary dysfunction and death of airway epithelial cells, whereas in stable COPD most studies have demonstrated an impairment in autophagy, with reduced autophagic flux and accumulation of abnormal mitochondria (defective mitophagy) and linked to cellular senescence. Autophagy may be increased or decreased in different cell types and depending on the cellular environment, making it difficult to target autophagy therapeutically. Several existing drugs may activate autophagy, including rapamycin, metformin, carbamazepine, cardiac glycosides and statins, whereas others, such as chloroquine, inhibit this process. However, these drugs are nonspecific and more selective drugs are now in development, which may prove useful as novel agents to treat asthma and COPD in the future.

Early Th2 inflammation in the upper respiratory mucosa as a predictor of severe COVID-19 and modulation by early treatment with inhaled corticosteroids: a mechanistic analysis.

BACKGROUND: Community-based clinical trials of the inhaled corticosteroid budesonide in early COVID-19 have shown improved patient outcomes. We aimed to understand the inflammatory mechanism of budesonide in the treatment of early COVID-19. METHODS: The STOIC trial was a randomised, open label, parallel group, phase 2 clinical intervention trial where patients were randomly assigned (1:1) to receive usual care (as needed antipyretics were only available treatment) or inhaled budesonide at a dose of 800 µg twice a day plus usual care. For this experimental analysis, we investigated the nasal mucosal inflammatory response in patients recruited to the STOIC trial and in a cohort of SARS-CoV-2-negative healthy controls, recruited from a long-term observational data collection study at the University of Oxford. In patients with SARS-CoV-2 who entered the STOIC study, nasal epithelial lining fluid was sampled at day of randomisation (day 0) and at day 14 following randomisation, blood samples were also collected at day 28 after randomisation. Nasal epithelial lining fluid and blood samples were collected from the SARS-CoV-2 negative control cohort. Inflammatory mediators in the nasal epithelial lining fluid and blood were assessed for a range of viral response proteins, and innate and adaptive response markers using Meso Scale Discovery enzyme linked immunoassay panels. These samples were used to investigate the evolution of inflammation in the early COVID-19 disease course and assess the effect of budesonide on inflammation. FINDINGS: 146 participants were recruited in the STOIC trial (n=73 in the usual care group; n=73 in the budesonide group). 140 nasal mucosal samples were available at day 0 (randomisation) and 122 samples at day 14. At day 28, whole blood was collected from 123 participants (62 in the budesonide group and 61 in the usual care group). 20 blood or nasal samples were collected from healthy controls. In early COVID-19 disease, there was an enhanced inflammatory airway response with the induction of an anti-viral and T-helper 1 and 2 (Th1/2) inflammatory response compared with healthy individuals. Individuals with COVID-19 who clinically deteriorated (ie, who met the primary outcome) showed an early blunted respiratory interferon response and pronounced and persistent Th2 inflammation, mediated by CC chemokine ligand (CCL)-24, compared with those with COVID-19 who did not clinically deteriorate. Over time, the natural course of COVID-19 showed persistently high respiratory interferon concentrations and elevated concentrations of the eosinophil chemokine, CCL-11, despite clinical symptom improvement. There was persistent systemic inflammation after 28 days following COVID-19, including elevated concentrations of interleukin (IL)-6, tumour necrosis factor-α, and CCL-11. Budesonide treatment modulated inflammation in the nose and blood and was shown to decrease IL-33 and increase CCL17. The STOIC trial was registered with ClinicalTrials.gov, NCT04416399. INTERPRETATION: An initial blunted interferon response and heightened T-helper 2 inflammatory response in the respiratory tract following SARS-CoV-2 infection could be a biomarker for predicting the development of severe COVID-19 disease. The clinical benefit of inhaled budesonide in early COVID-19 is likely to be as a consequence of its inflammatory modulatory effect, suggesting efficacy by reducing epithelial damage and an improved T-cell response. FUNDING: Oxford National Institute of Health Research Biomedical Research Centre and AstraZeneca.

The impact of birthweight on subsequent phenotype of type 2 diabetes in later life.

AIMS: It is well established that low birthweight is associated with subsequent risk of type 2 diabetes (T2DM). The aim of our study was to use a large birth cohort linked to a national diabetes registry to investigate how birthweight impacts the phenotype at diagnosis of T2DM and the subsequent rate of glycaemic deterioration. METHODS: We linked the Walker Birth Cohort (48,000 births, 1952-1966, Tayside, Scotland) to the national diabetes registry in Scotland (SCI-Diabetes). Birthweight was adjusted for gestational age. Simple linear regression was performed to assess the impact of the adjusted birthweight on the diabetes phenotype at diagnosis. This was then built up into a multiple regression model to allow for the adjustment of confounding variables. A cox proportional hazards model was then used to evaluate the impact of birthweight on diabetes progression. RESULTS: Lower birthweights were associated with a 293 day younger age of diagnosis of T2DM per 1 kg reduction in birthweight, p = 0.005; and a 1.29 kg/m2 lower BMI at diagnosis per 1 kg reduction in birthweight, p < 0.001. There was no significant association of birthweight on diabetes progression. CONCLUSION: For the first time, we have shown that a lower birthweight is associated with younger onset of T2DM, with those with lower birthweight also being slimmer at diagnosis. These results suggest that lower birthweight impacts on T2DM phenotype via reduced beta-cell function rather than insulin resistance.

Women's health behaviour change after receiving breast cancer risk estimates with tailored screening and prevention recommendations.

BACKGROUND: The Predicting Risk of Cancer at Screening (PROCAS) study provided women who were eligible for breast cancer screening in Greater Manchester (United Kingdom) with their 10-year risk of breast cancer, i.e., low (≤1.5%), average (1.5-4.99%), moderate (5.-7.99%) or high (≥8%). The aim of this study is to explore which factors were associated with women's uptake of screening and prevention recommendations. Additionally, we evaluated women's organisational preferences regarding tailored screening. METHODS: A total of 325 women with a self-reported low (n = 60), average (n = 125), moderate (n = 80), or high (n = 60) risk completed a two-part web-based survey. The first part contained questions about personal characteristics. For the second part women were asked about uptake of early detection and preventive behaviours after breast cancer risk communication. Additional questions were posed to explore preferences regarding the organisation of risk-stratified screening and prevention. We performed exploratory univariable and multivariable regression analyses to assess which factors were associated with uptake of primary and secondary breast cancer preventive behaviours, stratified by breast cancer risk. Organisational preferences are presented using descriptive statistics. RESULTS: Self-reported breast cancer risk predicted uptake of (a) supplemental screening and breast self-examination, (b) risk-reducing medication and (c) preventive lifestyle behaviours. Further predictors were (a) having a first degree relative with breast cancer, (b) higher age, and (c) higher body mass index (BMI). Women's organisational preferences for tailored screening emphasised a desire for more intensive screening for women at increased risk by further shortening the screening interval and moving the starting age forward. CONCLUSIONS: Breast cancer risk communication predicts the uptake of key tailored primary and secondary preventive behaviours. Effective communication of breast cancer risk information is essential to optimise the population-wide impact of tailored screening.