Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Knockout of syntaxin-4 in 3T3-L1 adipocytes reveals new insight into GLUT4 trafficking and adiponectin secretion.

Adipocytes are key to metabolic regulation, exhibiting insulin-stimulated glucose transport that is underpinned by the insulin-stimulated delivery of glucose transporter type 4 (SLC2A4, also known and hereafter referred to as GLUT4)-containing vesicles to the plasma membrane where they dock and fuse, and increase cell surface GLUT4 levels. Adipocytokines, such as adiponectin, are secreted via a similar mechanism. We used genome editing to knock out syntaxin-4, a protein reported to mediate fusion between GLUT4-containing vesicles and the plasma membrane in 3T3-L1 adipocytes. Syntaxin-4 knockout reduced insulin-stimulated glucose transport and adiponectin secretion by ∼50% and reduced GLUT4 levels. Ectopic expression of haemagglutinin (HA)-tagged GLUT4 conjugated to GFP showed that syntaxin-4-knockout cells retain significant GLUT4 translocation capacity, demonstrating that syntaxin-4 is dispensable for insulin-stimulated GLUT4 translocation. Analysis of recycling kinetics revealed only a modest reduction in the exocytic rate of GLUT4 in knockout cells, and little effect on endocytosis. These analyses demonstrate that syntaxin-4 is not always rate limiting for GLUT4 delivery to the cell surface. In sum, we show that syntaxin-4 knockout results in reduced insulin-stimulated glucose transport, depletion of cellular GLUT4 levels and inhibition of adiponectin secretion but has only modest effects on the translocation capacity of the cells. This article has an associated First Person interview with Hannah L. Black and Rachel Livingstone, joint first authors of the paper.

The top 10 priorities in adults living with type 1 diabetes in Ireland and the United Kingdom - A James Lind Alliance priority setting partnership.

AIMS: To undertake a Priority Setting Partnership (PSP), identifying the most important unanswered questions in type 1 diabetes in Ireland and the United Kingdom and to compare these to priorities identified in a 2011 PSP. METHODS: A steering committee (including eight individuals with lived experience/charity representatives and six clinicians) designed a survey which asked stakeholders to list three questions about type 1 diabetes. This was disseminated through social media, direct email contact, and printed posters. Following analysis, a second survey asked participants to rank these priorities in order of importance. The top questions were then carried forward to an online, 2 days final workshop where the final top 10 were ranked. RESULTS: There were 1050 responses (64% female, 78% adults living with type 1 diabetes, 9% healthcare professionals, 9% family members) to the first survey and 2937 individual questions were submitted. Sixty-five summary questions were submitted into a second survey, completed by 497 individuals (76% adults living with type 1 diabetes, 9% healthcare professionals, and 11% family members). Nineteen questions from the interim survey progressed to a final workshop, which identified the top 10 priorities through group discussion. As in 2011, there was emphasis on psychological health, diabetes-related complications, and hypoglycaemia. New themes prioritised included artificial intelligence and women's health. CONCLUSIONS: The research priorities, which have been identified using a robust and proven methodology, highlight the key concerns of those living with type 1 diabetes, their families and representatives, as well as clinicians in Ireland and the UK.

Metformin beyond type 2 diabetes: Emerging and potential new indications.

Metformin is best known as a foundational therapy for type 2 diabetes but is also used in other contexts in clinical medicine with a number of emerging and potential indications. Many of its beneficial effects may be mediated by modest effects on weight loss and insulin sensitivity, but it has multiple other known mechanisms of action. Current clinical uses beyond type 2 diabetes include: polycystic ovarian syndrome; diabetes in pregnancy/gestational diabetes; prevention of type 2 diabetes in prediabetes; and adjunct therapy in type 1 diabetes. As metformin has been in clinical use for almost 70 years, much of the underpinning evidence for its use in these conditions is, by definition, based on trials conducted before the advent of contemporary evidence-based medicine. As a result, some of the above-established uses are 'off-label' in many regulatory territories and their use varies accordingly in different countries. Going forward, several current 'repurposing' investigational uses of metformin are also being investigated: prevention of cancer (including in Li Fraumeni syndrome), renal protection, Alzheimer's disease, metabolic dysfunction-associated steatotic liver disease and promotion of healthy ageing. Despite the longevity of metformin and its important current roles beyond type 2 diabetes in clinical medicine, it has further potential and much research is ongoing.

Does metformin reduce the risk of cancer in obesity and diabetes? A systematic review and meta-analysis.

AIM: To evaluate the effect of metformin on cancer incidence in subjects with overweight/obesity and/or prediabetes/diabetes. MATERIALS AND METHODS: We searched MEDLINE, Embase and CENTRAL for randomized controlled trials (RCTs) in adults with overweight/obesity and/or prediabetes/diabetes that compared metformin to other interventions for ≥24 weeks. Independent reviewers selected and extracted data including population and intervention characteristics and new diagnoses of cancer. We used the RoB 2.0 risk-of-bias tool and the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework to assess risk of bias and certainty of evidence. RESULTS: From 14 895 records after removal of duplicates, 27 trials were included, providing a total of 10 717 subjects in the metformin group and 10 003 in the control group, with 170 and 208 new cases of cancer, respectively. Using a random-effects model, the relative risk was 1.07 (95% confidence interval 0.87-1.31), with similar results in subgroup analyses by study duration or effect of control intervention on weight. Risk of bias in most studies was low, and no evidence of publication bias was found. Trial sequential analysis provided evidence that the cumulative sample size was large enough to exclude a significant effect of metformin on cancer incidence. CONCLUSIONS: Metformin did not reduce cancer incidence in RCTs involving subjects with overweight/obesity and/or prediabetes/diabetes.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

AIMS/HYPOTHESIS: The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. METHODS: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). RESULTS: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. CONCLUSIONS/INTERPRETATION: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

Automated insulin delivery: benefits, challenges, and recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association.

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialised for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organisations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organisation has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonisation of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organisations for confirmation.

Genetic Landscape of the ACE2 Coronavirus Receptor.

BACKGROUND: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. METHODS: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. RESULTS: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. CONCLUSIONS: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.

Effect of Empagliflozin on Left Ventricular Volumes in Patients With Type 2 Diabetes, or Prediabetes, and Heart Failure With Reduced Ejection Fraction (SUGAR-DM-HF).

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors reduce the risk of heart failure hospitalization and cardiovascular death in patients with heart failure and reduced ejection fraction (HFrEF). However, their effects on cardiac structure and function in HFrEF are uncertain. METHODS: We designed a multicenter, randomized, double-blind, placebo-controlled trial (the SUGAR-DM-HF trial [Studies of Empagliflozin and Its Cardiovascular, Renal and Metabolic Effects in Patients With Diabetes Mellitus, or Prediabetes, and Heart Failure]) to investigate the cardiac effects of empagliflozin in patients in New York Heart Association functional class II to IV with a left ventricular (LV) ejection fraction ≤40% and type 2 diabetes or prediabetes. Patients were randomly assigned 1:1 to empagliflozin 10 mg once daily or placebo, stratified by age (<65 and ≥65 years) and glycemic status (diabetes or prediabetes). The coprimary outcomes were change from baseline to 36 weeks in LV end-systolic volume indexed to body surface area and LV global longitudinal strain both measured using cardiovascular magnetic resonance. Secondary efficacy outcomes included other cardiovascular magnetic resonance measures (LV end-diastolic volume index, LV ejection fraction), diuretic intensification, symptoms (Kansas City Cardiomyopathy Questionnaire Total Symptom Score, 6-minute walk distance, B-lines on lung ultrasound, and biomarkers (including N-terminal pro-B-type natriuretic peptide). RESULTS: From April 2018 to August 2019, 105 patients were randomly assigned: mean age 68.7 (SD, 11.1) years, 77 (73.3%) male, 82 (78.1%) diabetes and 23 (21.9%) prediabetes, mean LV ejection fraction 32.5% (9.8%), and 81 (77.1%) New York Heart Association II and 24 (22.9%) New York Heart Association III. Patients received standard treatment for HFrEF. In comparison with placebo, empagliflozin reduced LV end-systolic volume index by 6.0 (95% CI, -10.8 to -1.2) mL/m2 (P=0.015). There was no difference in LV global longitudinal strain. Empagliflozin reduced LV end-diastolic volume index by 8.2 (95% CI, -13.7 to -2.6) mL/m2 (P=0.0042) and reduced N-terminal pro-B-type natriuretic peptide by 28% (2%-47%), P=0.038. There were no between-group differences in other cardiovascular magnetic resonance measures, diuretic intensification, Kansas City Cardiomyopathy Questionnaire Total Symptom Score, 6-minute walk distance, or B-lines. CONCLUSIONS: The sodium-glucose cotransporter 2 inhibitor empagliflozin reduced LV volumes in patients with HFrEF and type 2 diabetes or prediabetes. Favorable reverse LV remodeling may be a mechanism by which sodium-glucose cotransporter 2 inhibitors reduce heart failure hospitalization and mortality in HFrEF. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03485092.

Quantitative levels of serum N-glycans in type 1 diabetes and their association with kidney disease.

We investigated associations of quantitative levels of N-glycans with hemoglobin A1c (HbA1c), renal function and renal function decline in type 1 diabetes. We measured 46 total N-glycan peaks (GPs) on 1565 serum samples from the Scottish Diabetes Research Network Type 1 Bioresource Study (SDRNT1BIO) and a pool of healthy donors. Quantitation of absolute abundance of each GP used 2AB-labeled mannose-3 as a standard. We studied cross-sectional associations of GPs and derived measures with HbA1c, albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), and prospective associations with incident albuminuria and final eGFR. All GPs were 1.4 to 3.2 times more abundant in SDRTN1BIO than in the healthy samples. Absolute levels of all GPs were slightly higher with higher HbA1c, with strongest associations for triantennary trigalactosylated disialylated, triantennary trigalactosylated trisialylated structures with core or outer arm fucose, and tetraantennary tetragalactosylated trisialylated glycans. Most GPs showed increased abundance with worsening ACR. Lower eGFR was associated with higher absolute GP levels, most significantly with biantennary digalactosylated disialylated glycans with and without bisect, triantennary trigalactosylated trisialylated glycans with and without outer arm fucose, and core fucosylated biantennary monogalactosylated monosialylated glycans. Although several GPs were inversely associated prospectively with final eGFR, cross-validated multivariable models did not improve prediction beyond clinical covariates. Elevated HbA1c is associated with an altered N-glycan profile in type 1 diabetes. Although we could not establish GPs to be prognostic of future renal function decline independently of HbA1c, further studies to evaluate their impact in the pathogenesis of diabetic kidney disease are warranted.

Metformin and carotid intima-media thickness in never-smokers with type 1 diabetes: The REMOVAL trial.

AIM: To determine whether metformin's effects on carotid artery intima-media thickness (cIMT) in type 1 diabetes differ according to smoking status. METHODS: Regression model effect estimates for the effect of metformin versus placebo (double-blind) on carotid IMT were calculated as a subgroup analysis of the REMOVAL trial. RESULTS: In 428 randomized participants (227 never-smokers, 201 ever-smokers), averaged mean carotid IMT progression (per year) was reduced by metformin versus placebo in never-smokers (-0.012 mm, 95% CI -0.021 to -0.002; p = .0137) but not in ever-smokers (0.003 mm, 95% CI -0.008 to 0.014; p = .5767); and similarly in non-current smokers (-0.008 mm, 95% CI -0.015 to -0.00001; p = .0497) but not in current smokers (0.013 mm, 95% CI -0.007 to 0.032; p = .1887). Three-way interaction terms (treatment*time*smoking status) were significant for never versus ever smoking (p = .0373, prespecified) and non-current versus current smoking (p = .0496, exploratory). Averaged maximal carotid IMT progression (per year) was reduced by metformin versus placebo in never-smokers (-0.020 mm, 95% CI -0.034 to -0.006; p = .0067) but not in ever-smokers (-0.006 mm, 95% CI -0.020 to 0.008; p = .4067), although this analysis was not supported by a significant three-way interaction term. CONCLUSIONS: This subgroup analysis of the REMOVAL trial provides additional support for a potentially wider role of adjunct metformin therapy in cardiovascular risk management in type 1 diabetes, particularly for individuals who have never smoked cigarettes.

Time in Range as a Research Outcome Measure.

Time in range (TIR) is gaining ground as an outcome measure in type 1 diabetes trials. However, inclusion of TIR raises several issues for trial design. In this article, the authors begin by defining TIR and describing the current international consensus around TIR targets. They then expand on evidence for the validity of TIR as a primary clinical trial outcome before concluding with some practical, ethical, and logistical implications.

Diabetes digital app technology: benefits, challenges, and recommendations. A consensus report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group.

Digital health technology, especially digital and health applications ('apps'), have been developing rapidly to help people manage their diabetes. Numerous health-related apps provided on smartphones and other wireless devices are available to support people with diabetes who need to adopt either lifestyle interventions or medication adjustments in response to glucose-monitoring data. However, regulations and guidelines have not caught up with the burgeoning field to standardise how mobile health apps are reviewed and monitored for patient safety and clinical validity. The available evidence on the safety and effectiveness of mobile health apps, especially for diabetes, remains limited. The European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) have therefore conducted a joint review of the current landscape of available diabetes digital health technology (only stand-alone diabetes apps, as opposed to those that are integral to a regulated medical device, such as insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems) and practices of regulatory authorities and organisations. We found that, across the USA and Europe, mobile apps intended to manage health and wellness are largely unregulated unless they meet the definition of medical devices for therapeutic and/or diagnostic purposes. International organisations, including the International Medical Device Regulators Forum and WHO, have made strides in classifying different types of digital health technology and integrating digital health technology into the field of medical devices. As the diabetes digital health field continues to develop and become more fully integrated into everyday life, we wish to ensure that it is based on the best evidence for safety and efficacy. As a result, we bring to light several issues that the diabetes community, including regulatory authorities, policymakers, professional organisations, researchers, people with diabetes and healthcare professionals, needs to address to ensure that diabetes health technology can meet its full potential. These issues range from inadequate evidence on app accuracy and clinical validity to lack of training provision, poor interoperability and standardisation, and insufficient data security. We conclude with a series of recommended actions to resolve some of these shortcomings.

Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus.

AIMS/HYPOTHESIS: The aim of this study was to provide data from a contemporary population-representative cohort on rates and predictors of renal decline in type 1 diabetes. METHODS: We used data from a cohort of 5777 people with type 1 diabetes aged 16 and older, diagnosed before the age of 50, and representative of the adult population with type 1 diabetes in Scotland (Scottish Diabetes Research Network Type 1 Bioresource; SDRNT1BIO). We measured serum creatinine and urinary albumin/creatinine ratio (ACR) at recruitment and linked the data to the national electronic healthcare records. RESULTS: Median age was 44.1 years and diabetes duration 20.9 years. The prevalence of CKD stages G1, G2, G3 and G4 and end-stage renal disease (ESRD) was 64.0%, 29.3%, 5.4%, 0.6%, 0.7%, respectively. Micro/macroalbuminuria prevalence was 8.6% and 3.0%, respectively. The incidence rate of ESRD was 2.5 (95% CI 1.9, 3.2) per 1000 person-years. The majority (59%) of those with chronic kidney disease stages G3-G5 did not have albuminuria on the day of recruitment or previously. Over 11.6 years of observation, the median annual decline in eGFR was modest at -1.3 ml min-1 [1.73 m]-2 year-1 (interquartile range [IQR]: -2.2, -0.4). However, 14% experienced a more significant loss of at least 3 ml min-1 [1.73 m]-2. These decliners had more cardiovascular disease (OR 1.9, p = 5 × 10-5) and retinopathy (OR 1.3 p = 0.02). Adding HbA1c, prior cardiovascular disease, recent mean eGFR and prior trajectory of eGFR to a model with age, sex, diabetes duration, current eGFR and ACR maximised the prediction of final eGFR (r2 increment from 0.698 to 0.745, p < 10-16). Attempting to model nonlinearity in eGFR decline or to detect latent classes of decliners did not improve prediction. CONCLUSIONS: These data show much lower levels of kidney disease than historical estimates. However, early identification of those destined to experience significant decline in eGFR remains challenging.

Time trends in deaths before age 50 years in people with type 1 diabetes: a nationwide analysis from Scotland 2004-2017.

AIMS/HYPOTHESIS: We aimed to examine whether crude mortality and mortality relative to the general population below 50 years of age have improved in recent years in those with type 1 diabetes. METHODS: Individuals with type 1 diabetes aged below 50 and at least 1 year old at any time between 2004 and 2017 in Scotland were identified using the national register. Death data were obtained by linkage to Scottish national death registrations. Indirect age standardisation was used to calculate sex-specific standardised mortality ratios (SMRs). Poisson regression was used to test for calendar-time effects as incidence rate ratios (IRRs). RESULTS: There were 1138 deaths in 251,143 person-years among 27,935 people with type 1 diabetes. There was a significant decline in mortality rate over time (IRR for calendar year 0.983 [95% CI 0.967, 0.998], p = 0.03), but the SMR remained approximately stable at 3.1 and 3.6 in men and 4.09 and 4.16 in women for 2004 and 2017, respectively. Diabetic ketoacidosis or coma (DKAoC) accounted for 22% of deaths and the rate did not decline significantly (IRR 0.975 [95% CI 0.94, 1.011], p = 0.168); 79.3% of DKAoC deaths occurred out of hospital. Circulatory diseases accounted for 27% of deaths and did decline significantly (IRR 0.946 [95% CI 0.914, 0.979], p = 0.002). CONCLUSIONS/INTERPRETATION: Absolute mortality has fallen, but the relative impact of type 1 diabetes on mortality below 50 years has not improved. There is scope to improve prevention of premature circulatory diseases and DKAoC and to develop more effective strategies for enabling people with type 1 diabetes to avoid clinically significant hyper- or hypoglycaemia. Graphical abstract.

Comparison of serum and urinary biomarker panels with albumin/creatinine ratio in the prediction of renal function decline in type 1 diabetes.

AIMS/HYPOTHESIS: We examined whether candidate biomarkers in serum or urine can improve the prediction of renal disease progression in type 1 diabetes beyond prior eGFR, comparing their performance with urinary albumin/creatinine ratio (ACR). METHODS: From the population-representative Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) we sampled 50% and 25% of those with starting eGFR below and above 75 ml min-1 [1.73 m]-2, respectively (N = 1629), and with median 5.1 years of follow-up. Multiplexed ELISAs and single molecule array technology were used to measure nine serum biomarkers and 13 urine biomarkers based on our and others' prior work using large discovery and candidate studies. Associations with final eGFR and with progression to <30 ml min-1 [1.73] m-2, both adjusted for baseline eGFR, were tested using linear and logistic regression models. Parsimonious biomarker panels were identified using a penalised Bayesian approach, and their performance was evaluated through tenfold cross-validation and compared with using urinary ACR and other clinical record data. RESULTS: Seven serum and seven urine biomarkers were strongly associated with either final eGFR or progression to <30 ml min-1 [1.73 m]-2, adjusting for baseline eGFR and other covariates (all at p<2.3 × 10-3). Of these, associations of four serum biomarkers were independent of ACR for both outcomes. The strongest associations with both final eGFR and progression to <30 ml min-1 [1.73 m]-2 were for serum TNF receptor 1, kidney injury molecule 1, CD27 antigen, α-1-microglobulin and syndecan-1. These serum associations were also significant in normoalbuminuric participants for both outcomes. On top of baseline covariates, the r2 for prediction of final eGFR increased from 0.702 to 0.743 for serum biomarkers, and from 0.702 to 0.721 for ACR alone. The area under the receiver operating characteristic curve for progression to <30 ml min-1 [1.73 m]-2 increased from 0.876 to 0.953 for serum biomarkers, and to 0.911 for ACR alone. Other urinary biomarkers did not outperform ACR. CONCLUSIONS/INTERPRETATION: A parsimonious panel of serum biomarkers easily measurable along with serum creatinine may outperform ACR for predicting renal disease progression in type 1 diabetes, potentially obviating the need for urine testing.

Metformin and cardiorenal outcomes in diabetes: A reappraisal.

The guidance issued to the pharmaceutical industry by the US Food and Drug Administration in 2008 has led to the publication of a series of randomized, controlled cardiovascular outcomes trials with newer therapeutic classes of glucose-lowering medications. Several of these trials, which evaluated the newer therapeutic classes of sodium-glucose co-transporter-2 inhibitors and glucagon-like peptide-1 receptor agonists, have reported a reduced incidence of major adverse cardiovascular and/or renal outcomes, usually relative to placebo and standard of care. Metformin was the first glucose-lowering agent reported to improve cardiovascular outcomes in the UK Prospective Diabetes Study (UKPDS) and thus became the foundation of standard care. However, as this clinical trial reported more than 20 years ago, differences from current standards of trial design and evaluation complicate comparison of the cardiovascular profiles of older and newer agents. Our article revisits the evidence for cardiovascular protection with metformin and reviews its effects on the kidney.

Glycaemic control trends in people with type 1 diabetes in Scotland 2004-2016.

AIMS/HYPOTHESIS: The aim of this work was to examine whether glycaemic control has improved in those with type 1 diabetes in Scotland between 2004 and 2016, and whether any trends differed by sociodemographic factors. METHODS: We analysed records from 30,717 people with type 1 diabetes, registered anytime between 2004 and 2016 in the national diabetes database, which contained repeated measures of HbA1c. An additive mixed regression model was used to estimate calendar time and other effects on HbA1c. RESULTS: Overall, median (IQR) HbA1c decreased from 72 (21) mmol/mol [8.7 (4.1)%] in 2004 to 68 (21) mmol/mol (8.4 [4.1]%) in 2016. However, all of the improvement across the period occurred in the latter 4 years: the regression model showed that the only period of significant change in HbA1c was 2012-2016 where there was a fall of 3 (95% CI 1.82, 3.43) mmol/mol. The largest reductions in HbA1c in this period were seen in children, from 69 (16) mmol/mol (8.5 [3.6]%) to 63 (14) mmol/mol (7.9 [3.4]%), and adolescents, from 75 (25) mmol/mol (9.0 [4.4]%) to 70 (23) mmol/mol (8.6 [4.3]%). Socioeconomic status (according to Scottish Index of Multiple Deprivation) affected the HbA1c values: from the regression model, the 20% of people living in the most-deprived areas had HbA1c levels on average 8.0 (95% CI 7.4, 8.9) mmol/mol higher than those of the 20% of people living in the least-deprived areas. However this difference did not change significantly over time. From the regression model HbA1c was on average 1.7 (95% CI 1.6, 1.8) mmol/mol higher in women than in men. This sex difference did not narrow over time. CONCLUSIONS/INTERPRETATION: In this high-income country, we identified a modest but important improvement in HbA1c since 2012 that was most marked in children and adolescents. These changes coincided with national initiatives to reduce HbA1c including an expansion of pump therapy. However, in most people, overall glycaemic control remains far from target levels and further improvement is badly needed, particularly in those from more-deprived areas.

The effect of dapagliflozin on glycaemic control and other cardiovascular disease risk factors in type 2 diabetes mellitus: a real-world observational study.

AIMS/HYPOTHESIS: Dapagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, is indicated for improving glycaemic control in type 2 diabetes mellitus. Whether its effects on HbA1c and other variables, including safety outcomes, in clinical trials are obtained in real-world practice needs to be established. METHODS: We used data from the comprehensive national diabetes register, the Scottish Care Information-Diabetes (SCI-Diabetes) collaboration database, available from 2004 to mid-2016. Data within this database were linked to mortality data from the General Registrar, available from the Information Services Division (ISD) of the National Health Service in Scotland. We calculated crude within-person differences between pre- and post-drug-initiation values of HbA1c, BMI, body weight, systolic blood pressure (SBP) and eGFR. We used mixed-effects regression models to adjust for within-person time trajectories in these measures. For completeness, we evaluated safety outcomes, cardiovascular disease events, lower-limb amputation and diabetic ketoacidosis, focusing on cumulative exposure effects, using Cox proportional hazard models, though power to detect such effects was limited. RESULTS: Among 8566 people exposed to dapagliflozin over a median of 210 days the crude within-person change in HbA1c was -10.41 mmol/mol (-0.95%) after 3 months' exposure. The crude change after 12 months was -12.99 mmol/mol (-1.19%) but considering the expected rise over time in HbA1c gave a dapagliflozin-exposure-effect estimate of -15.14 mmol/mol (95% CI -15.87, -14.41) (-1.39% [95% CI -1.45, -1.32]) at 12 months that was maintained thereafter. A drop in SBP of -4.32 mmHg (95% CI -4.84, -3.79) on exposure within the first 3 months was also maintained thereafter. Reductions in BMI and body weight stabilised by 6 months at -0.82 kg/m2 (95% CI -0.87, -0.77) and -2.20 kg (95% CI -2.34, -2.06) and were maintained thereafter. eGFR declined initially by -1.81 ml min-1 [1.73 m]-2 (95% CI -2.10, -1.52) at 3 months but varied thereafter. There were no significant effects of cumulative drug exposure on safety outcomes. CONCLUSIONS/INTERPRETATION: Dapagliflozin exposure was associated with reductions in HbA1c, SBP, body weight and BMI that were at least as large as in clinical trials. Dapagliflozin also prevented the expected rise in HbA1c and SBP over the period of study.

Persistent C-peptide secretion in Type 1 diabetes and its relationship to the genetic architecture of diabetes.

BACKGROUND: The objective of this cross-sectional study was to explore the relationship of detectable C-peptide secretion in type 1 diabetes to clinical features and to the genetic architecture of diabetes. METHODS: C-peptide was measured in an untimed serum sample in the SDRNT1BIO cohort of 6076 Scottish people with clinically diagnosed type 1 diabetes or latent autoimmune diabetes of adulthood. Risk scores at loci previously associated with type 1 and type 2 diabetes were calculated from publicly available summary statistics. RESULTS: Prevalence of detectable C-peptide varied from 19% in those with onset before age 15 and duration greater than 15 years to 92% in those with onset after age 35 and duration less than 5 years. Twenty-nine percent of variance in C-peptide levels was accounted for by associations with male gender, late age at onset and short duration. The SNP heritability of residual C-peptide secretion adjusted for gender, age at onset and duration was estimated as 26%. Genotypic risk score for type 1 diabetes was inversely associated with detectable C-peptide secretion: the most strongly associated loci were the HLA and INS gene regions. A risk score for type 1 diabetes based on the HLA DR3 and DQ8-DR4 serotypes was strongly associated with early age at onset and inversely associated with C-peptide persistence. For C-peptide but not age at onset, there were strong associations with risk scores for type 1 and type 2 diabetes that were based on SNPs in the HLA region but not accounted for by HLA serotype. CONCLUSIONS: Persistence of C-peptide secretion varies widely in people clinically diagnosed as type 1 diabetes. C-peptide persistence is influenced by variants in the HLA region that are different from those determining risk of early-onset type 1 diabetes. Known risk loci for diabetes account for only a small proportion of the genetic effects on C-peptide persistence.