Impact of COVID-19 and Non-COVID-19 Hospitalized Pneumonia on Longer-Term Cardiovascular Mortality in People With Type 2 Diabetes: A Nationwide Prospective Cohort Study From Scotland.

OBJECTIVE: In this study we examine whether hospitalized coronavirus disease 2019 (COVID-19) pneumonia increases long-term cardiovascular mortality more than other hospitalized pneumonias in people with type 2 diabetes and aim to quantify the relative cardiovascular disease (CVD) mortality risks associated with COVID-19 versus non-COVID-19 pneumonia. RESEARCH DESIGN AND METHODS: With use of the SCI-Diabetes register, two cohorts were identified: individuals with type 2 diabetes in 2016 and at the 2020 pandemic onset. Hospital and death records were linked for determination of pneumonia exposure and CVD deaths. Poisson regression estimated rate ratios (RRs) for CVD death associated with both pneumonia types, with adjustment for confounders. Median follow-up durations were 1,461 days (2016 cohort) and 700 days (2020 cohort). RESULTS: The adjusted RR for CVD death following non-COVID-19 pneumonia was 5.51 (95% CI 5.31-5.71) prepandemic and 7.3 (6.86-7.76) during the pandemic. For COVID-19 pneumonia, the RR was 9.13 (8.55-9.75). Beyond 30 days post pneumonia, the RRs converged, to 4.24 (3.90-4.60) for non-COVID-19 and 3.35 (3.00-3.74) for COVID-19 pneumonia, consistent even with exclusion of prior CVD cases. CONCLUSIONS: Hospitalized pneumonia, irrespective of causal agent, marks an increased risk for CVD death immediately and over the long-term. COVID-19 pneumonia poses a higher CVD death risk than other pneumonias in the short-term, but this distinction diminishes over time. These insights underscore the need for including pneumonia in CVD risk assessments, with particular attention to the acute impact of COVID-19 pneumonia.

Long-term kidney outcomes of semaglutide in obesity and cardiovascular disease in the SELECT trial.

The SELECT trial previously reported a 20% reduction in major adverse cardiovascular events with semaglutide (n = 8,803) versus placebo (n = 8,801) in patients with overweight/obesity and established cardiovascular disease, without diabetes. In the present study, we examined the effect of once-weekly semaglutide 2.4 mg on kidney outcomes in the SELECT trial. The incidence of the pre-specified main composite kidney endpoint (death from kidney disease, initiation of chronic kidney replacement therapy, onset of persistent estimated glomerular filtration rate (eGFR) < 15 ml min-1 1.73 m-2, persistent ≥50% reduction in eGFR or onset of persistent macroalbuminuria) was lower with semaglutide (1.8%) versus placebo (2.2%): hazard ratio (HR) = 0.78; 95% confidence interval (CI) 0.63, 0.96; P = 0.02. The treatment benefit at 104 weeks for eGFR was 0.75 ml min-1 1.73 m-2 (95% CI 0.43, 1.06; P < 0.001) overall and 2.19 ml min-1 1.73 m-2 (95% CI 1.00, 3.38; P < 0.001) in patients with baseline eGFR <60 ml min-1 1.73 m-2. These results suggest a benefit of semaglutide on kidney outcomes in individuals with overweight/obesity, without diabetes.ClinicalTrials.gov identifier: NCT03574597 .

Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial.

In the SELECT cardiovascular outcomes trial, semaglutide showed a 20% reduction in major adverse cardiovascular events in 17,604 adults with preexisting cardiovascular disease, overweight or obesity, without diabetes. Here in this prespecified analysis, we examined effects of semaglutide on weight and anthropometric outcomes, safety and tolerability by baseline body mass index (BMI). In patients treated with semaglutide, weight loss continued over 65 weeks and was sustained for up to 4 years. At 208 weeks, semaglutide was associated with mean reduction in weight (-10.2%), waist circumference (-7.7 cm) and waist-to-height ratio (-6.9%) versus placebo (-1.5%, -1.3 cm and -1.0%, respectively; P < 0.0001 for all comparisons versus placebo). Clinically meaningful weight loss occurred in both sexes and all races, body sizes and regions. Semaglutide was associated with fewer serious adverse events. For each BMI category (<30, 30 to <35, 35 to <40 and ≥40 kg m-2) there were lower rates (events per 100 years of observation) of serious adverse events with semaglutide (43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo). Semaglutide was associated with increased rates of trial product discontinuation. Discontinuations increased as BMI class decreased. In SELECT, at 208 weeks, semaglutide produced clinically significant weight loss and improvements in anthropometric measurements versus placebo. Weight loss was sustained over 4 years. ClinicalTrials.gov identifier: NCT03574597 .

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials.

AIMS/HYPOTHESIS: The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events. METHODS: Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed. RESULTS: For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period. CONCLUSIONS/INTERPRETATION: Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.

Ongoing burden and recent trends in severe hospitalised hypoglycaemia events in people with type 1 and type 2 diabetes in Scotland: A nationwide cohort study 2016-2022.

AIMS: We examined severe hospitalised hypoglycaemia (SHH) rates in people with type 1 and type 2 diabetes in Scotland during 2016-2022, stratifying by sociodemographics. METHODS: Using the Scottish National diabetes register (SCI-Diabetes), we identified people with type 1 and type 2 diabetes alive anytime during 2016-2022. SHH events were determined through linkage to hospital admission and death registry data. We calculated annual SHH rates overall and by age, sex, and socioeconomic status. Summary estimates of time and stratum effects were obtained by fitting adjusted generalised additive models using R package mgcv. RESULTS: Rates for those under 20 with type 1 diabetes reached their minimum at the 2020-2021 transition, 30% below the study period average. A gradual decline over time also occurred among 20-49-year-olds with type 1 diabetes. Overall, females had 15% higher rates than males with type 2 diabetes (rate ratio 1.15, 95% CI 1.08-1.22). People in the most versus least deprived quintile experienced 2.58 times higher rates (95% CI 2.27-2.93) in type 1 diabetes and 2.33 times higher (95% CI 2.08-2.62) in type 2 diabetes. CONCLUSIONS: Despite advances in care, SHH remains a significant problem in diabetes. Future efforts must address the large socioeconomic disparities in SHH risks.

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.

BACKGROUND/AIMS: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme. METHODS: We consider 21 346 and 247 233 people with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), respectively, each contributing on average 4.8 and 4.4 screening intervals of which 1339 and 4675 intervals concluded with a referable screening episode. Information extracted from fundus images using DL was used to predict referable status at the end of interval and its predictive value in comparison to screening-assigned DR grade was assessed. RESULTS: The DL predictor increased the area under the receiver operating characteristic curve in comparison to a predictor using current DR grades from 0.809 to 0.87 for T1DM and from 0.825 to 0.87 for T2DM. Expected sojourn time-the time from becoming referable to being rescreened-was found to be 3.4 (T1DM) and 2.7 (T2DM) weeks less for a DL-derived policy compared with the current recall policy. CONCLUSIONS: We showed that, compared with using the current retinopathy grade, DL of fundus images significantly improves the prediction of incident referable retinopathy before the next screening episode. This can impact screening recall interval policy positively, for example, by reducing the expected time with referable disease for a fixed workload-which we show as an exemplar. Additionally, it could be used to optimise workload for a fixed sojourn time.

Evidence for C-Peptide as a Validated Surrogate to Predict Clinical Benefits in Trials of Disease-Modifying Therapies for Type 1 Diabetes.

Type 1 diabetes is a chronic autoimmune disease in which destruction of pancreatic ß-cells causes life-threatening metabolic dysregulation. Numerous approaches are envisioned for new therapies, but limitations of current clinical outcome measures are significant disincentives to development efforts. C-peptide, a direct byproduct of proinsulin processing, is a quantitative biomarker of ß-cell function that is not cleared by the liver and can be measured in the peripheral blood. Studies of quantitative measures of ß-cell function have established a predictive relationship between stimulated C-peptide as a measure of ß-cell function and clinical benefits. C-peptide levels at diagnosis are often high enough to afford glycemic control benefits associated with protection from end-organ complications of diabetes, and even lower levels offer protection from severe hypoglycemia in type 1 diabetes, as observed in large prospective cohort studies and interventional trials of islet transplantation. These observations support consideration of C-peptide not just as a biomarker of ß-cell function but also as a specific, sensitive, feasible, and clinically meaningful outcome defining ß-cell preservation or restoration for clinical trials of disease-modifying therapies. Regulatory acceptance of C-peptide as a validated surrogate for demonstration of efficacy would greatly facilitate development of disease-modifying therapies for type 1 diabetes.

Type 2 diabetes exacerbates changes in blood pressure-independent arterial stiffness: cross-sectional and longitudinal evidence from the SUMMIT study.

Greater central artery stiffness is observed in people with type 2 diabetes (T2DM). Elevated blood pressure (BP) and altered arterial wall structure/composition in T2DM are generally considered as main drivers for this alteration. However, because conventional arterial stiffness measures are BP-dependent and as such an influence of BP remains in a measure, it is unclear if greater central artery stiffness is a function of greater BP, or due to changes in the structure and composition of the arterial wall. We aimed to measure BP-independent arterial stiffness (ß0) cross-sectionally and longitudinally in T2DM. We studied 753 adults with T2DM (DM+) and 436 adults without (DM-) at baseline (Phase 1), and 310 DM+ and 210 DM- adults at 3-yr follow-up (Phase 2). We measured carotid-femoral pulse wave velocity and used it to calculate ß0. In Phase 1, ß0 was significantly greater in DM+ than DM- after adjusting for age and sex [27.5 (26.6-28.3) vs. 23.6 (22.4-24.8) au, P < 0.001]. Partial correlation analyses after controlling for age and sex showed that ß0 was significantly associated with hemoglobin A1c (r = 0.15 P < 0.001) and heart rate [(HR): r = 0.23 P < 0.001)] in DM+. In Phase 2, percentage-change in ß0 was significantly greater in DM+ than DM- [19.5 (14.9-24.0) vs. 5.0 (-0.6 to 10.6) %, P < 0.001] after adjusting for age, sex, and baseline ß0. ß0 was greater in DM+ than DM- and increased much more in DM+ than in DM- over 3 yr. This suggests that T2DM exacerbates BP-independent arterial stiffness and may have a complemental utility to existing arterial stiffness indices.NEW & NOTEWORTHY We demonstrate in this study a greater BP-independent arterial stiffness ß0 in people with type 2 diabetes (T2DM) compared to those without, and also a greater change in ß0 over 3 yr in people with T2DM than those without. These findings suggest that the intrinsic properties of the arterial wall may change in a different and more detrimental way in people with T2DM and likely represents accumulation of cardiovascular risk.

Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes.

BACKGROUND: Semaglutide, a glucagon-like peptide-1 receptor agonist, has been shown to reduce the risk of adverse cardiovascular events in patients with diabetes. Whether semaglutide can reduce cardiovascular risk associated with overweight and obesity in the absence of diabetes is unknown. METHODS: In a multicenter, double-blind, randomized, placebo-controlled, event-driven superiority trial, we enrolled patients 45 years of age or older who had preexisting cardiovascular disease and a body-mass index (the weight in kilograms divided by the square of the height in meters) of 27 or greater but no history of diabetes. Patients were randomly assigned in a 1:1 ratio to receive once-weekly subcutaneous semaglutide at a dose of 2.4 mg or placebo. The primary cardiovascular end point was a composite of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke in a time-to-first-event analysis. Safety was also assessed. RESULTS: A total of 17,604 patients were enrolled; 8803 were assigned to receive semaglutide and 8801 to receive placebo. The mean (±SD) duration of exposure to semaglutide or placebo was 34.2±13.7 months, and the mean duration of follow-up was 39.8±9.4 months. A primary cardiovascular end-point event occurred in 569 of the 8803 patients (6.5%) in the semaglutide group and in 701 of the 8801 patients (8.0%) in the placebo group (hazard ratio, 0.80; 95% confidence interval, 0.72 to 0.90; P<0.001). Adverse events leading to permanent discontinuation of the trial product occurred in 1461 patients (16.6%) in the semaglutide group and 718 patients (8.2%) in the placebo group (P<0.001). CONCLUSIONS: In patients with preexisting cardiovascular disease and overweight or obesity but without diabetes, weekly subcutaneous semaglutide at a dose of 2.4 mg was superior to placebo in reducing the incidence of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke at a mean follow-up of 39.8 months. (Funded by Novo Nordisk; SELECT ClinicalTrials.gov number, NCT03574597.).

Rationale and design of a randomised phase III registration trial investigating finerenone in participants with type 1 diabetes and chronic kidney disease: The FINE-ONE trial.

AIMS: Despite guideline-recommended treatments, including renin angiotensin system inhibition, up to 40 % of individuals with type 1 diabetes develop chronic kidney disease (CKD) putting them at risk of kidney failure. Finerenone is approved to reduce the risk of kidney failure in individuals with type 2 diabetes. We postulate that finerenone will demonstrate benefits on kidney outcomes in people with type 1 diabetes. METHODS: FINE-ONE (NCT05901831) is a randomised, placebo-controlled, double-blind phase III trial of 7.5 months' duration in ∼220 adults with type 1 diabetes, urine albumin/creatinine ratio (UACR) of ≥ 200-< 5000 mg/g (≥ 22.6-< 565 mg/mmol) and eGFR of ≥ 25-< 90 ml/min/1.73 m2. RESULTS: The primary endpoint is relative change in UACR from baseline over 6 months. UACR is used as a bridging biomarker (BB), since the treatment effect of finerenone on UACR was associated with its efficacy on kidney outcomes in the type 2 diabetes trials. Based on regulatory authority feedback, UACR can be used as a BB for kidney outcomes to support registration of finerenone in type 1 diabetes, provided necessary criteria are met. Secondary outcomes include incidences of treatment-emergent adverse events, treatment-emergent serious adverse events and hyperkalaemia. CONCLUSIONS: FINE-ONE will evaluate the efficacy and safety of finerenone in type 1 diabetes and CKD. Finerenone could become the first registered treatment for CKD associated with type 1 diabetes in almost 30 years. TRIAL REGISTRATION: ClinicalTrials.gov NCT05901831.

Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme.

BACKGROUND/AIMS: Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradable with no DR versus manual grading required. The study aim was to develop a deep learning-based autograder using images and gradings from DES and to compare its performance with that of iGradingM. METHODS: Retinal images, quality assurance (QA) data and routine DR grades were obtained from national datasets in 179 944 patients for years 2006-2016. QA grades were available for 744 images. We developed a deep learning-based algorithm to detect whether either eye contained ungradable images or any DR. The sensitivity and specificity were evaluated against consensus QA grades and routine grades. RESULTS: Images used in QA which were ungradable or with DR were detected by deep learning with better specificity compared with manual graders (p<0.001) and with iGradingM (p<0.001) at the same sensitivities. Any DR according to the DES final grade was detected with 89.19% (270 392/303 154) sensitivity and 77.41% (500 945/647 158) specificity. Observable disease and referable disease were detected with sensitivities of 96.58% (16 613/17 201) and 98.48% (22 600/22 948), respectively. Overall, 43.84% of screening episodes would require manual grading. CONCLUSION: A deep learning-based system for DR grading was evaluated in QA data and images from 11 years in 50% of people attending a national DR screening programme. The system could reduce the manual grading workload at the same sensitivity compared with the current automated grading system.

Dulaglutide and Kidney Function-Related Outcomes in Type 2 Diabetes: A REWIND Post Hoc Analysis.

OBJECTIVE: Dulaglutide (DU) 1.5 mg was associated with improved composite renal outcomes that included new-onset macroalbuminuria in people with type 2 diabetes with previous cardiovascular disease or cardiovascular risk factors in the REWIND (Researching cardiovascular Events with a Weekly INcretin in Diabetes) trial. This exploratory post hoc analysis evaluated kidney function-related outcomes, excluding the new-onset macroalbuminuria component, among the REWIND participants. RESEARCH DESIGN AND METHODS: Intent-to-treat analyses were performed on REWIND participants (n = 4,949 DU, n = 4,952 placebo). Time to occurrence of a composite kidney function-related outcome (≥40% sustained decline in estimated glomerular filtration rate [eGFR], per the Chronic Kidney Disease Epidemiology Collaboration 2009 equation, end-stage renal disease, or renal-related death), and mean annual eGFR slope were examined. Analyses were conducted overall and within subgroups defined by baseline urinary albumin-to-creatinine ratio (UACR <30 or ≥30 mg/g) and baseline eGFR (<60 or ≥60 mL/min/1.73 m2). RESULTS: The post hoc composite kidney function-related outcome occurred less frequently among participants assigned to DU than placebo (hazard ratio [HR] 0.75, 95% CI 0.62-0.92, P = 0.004), with no evidence of a differential DU treatment effect by UACR or eGFR subgroup. A ≥40% sustained eGFR decline occurred less frequently among participants assigned to DU than placebo (HR 0.72, 95% CI 0.58-0.88, P = 0.002). The mean annual decline in eGFR slope was significantly smaller for participants assigned to DU than placebo (-1.37 vs. -1.56 mL/min/1.73 m2/year, P < 0.001); results were similar for all subgroups. CONCLUSIONS: The estimated 25% reduced hazard of a kidney function-related outcome among participants assigned to DU highlights its potential for delaying or slowing the development of diabetic kidney disease in people with type 2 diabetes.

Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.

AIMS: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD). METHODS: DL models were trained to jointly predict future CVD risk and CVD risk factors and used to output a DL score. Poisson regression models including clinical risk factors with and without a DL score were fitted to study cohorts with 2,072 and 38,730 incident CVD events in type 1 (T1DM) and type 2 diabetes (T2DM) respectively. RESULTS: DL scores were independently associated with incident CVD with adjusted standardised incidence rate ratios of 1.14 (P = 3 × 10-04 95 % CI (1.06, 1.23)) and 1.16 (P = 4 × 10-33 95 % CI (1.13, 1.18)) in T1DM and T2DM cohorts respectively. The differences in predictive performance between models with and without a DL score were statistically significant (differences in test log-likelihood 6.7 and 51.1 natural log units) but the increments in C-statistics from 0.820 to 0.822 and from 0.709 to 0.711 for T1DM and T2DM respectively, were small. CONCLUSIONS: These results show that in people with diabetes, retinal photographs contain information on future CVD risk. However for this to contribute appreciably to clinical prediction of CVD further approaches, including exploitation of serial images, need to be evaluated.

Genome-wide aggregated trans-effects on risk of type 1 diabetes: A test of the "omnigenic" sparse effector hypothesis of complex trait genetics.

The "omnigenic" hypothesis postulates that the polygenic effects of common SNPs on a typical complex trait are mediated through trans-effects on expression of a relatively sparse set of effector ("core") genes. We tested this hypothesis in a study of 4,964 cases of type 1 diabetes (T1D) and 7,497 controls by using summary statistics to calculate aggregated (excluding the HLA region) trans-scores for gene expression in blood. From associations of T1D with aggregated trans-scores, nine putative core genes were identified, of which three-STAT1, CTLA4 and FOXP3-are genes in which variants cause monogenic forms of autoimmune diabetes. Seven of these genes affect the activity of regulatory T cells, and two are involved in immune responses to microbial lipids. Four T1D-associated genomic regions could be identified as master regulators via trans-effects on gene expression. These results support the sparse effector hypothesis and reshape our understanding of the genetic architecture of T1D.

Biomarker Changes Associated With Both Dulaglutide and Cardiovascular Events in the REWIND Randomized Controlled Trial: A Nested Case-Control Post Hoc Analysis.

OBJECTIVE: The glucagon-like peptide-1 receptor agonist dulaglutide reduced MACE in the Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) trial. This article expores the relationship of selected biomarkers to both dulaglutide and major adverse cardiovascular events (MACE). RESEARCH DESIGN AND METHODS: In this post hoc analysis, stored fasting baseline and 2-year plasma samples from 824 REWIND participants with MACE during follow-up and 845 matched non-MACE participants were analyzed for 2-year changes in 19 protein biomarkers. Two-year changes in 135 metabolites were also analyzed in 600 participants with MACE during follow-up and in 601 matched non-MACE participants. Linear and logistic regression models were used to identify proteins that were associated with both dulaglutide treatment and MACE. Similar models were used to identify metabolites that were associated with both dulaglutide treatment and MACE. RESULTS: Compared with placebo, dulaglutide was associated with a greater reduction or lesser 2-year rise from baseline in N-terminal prohormone of brain natriuretic peptide (NT-proBNP), growth differentiation factor 15 (GDF-15), high-sensitivity C-reactive protein, and a greater 2-year rise in C-peptide. Compared with placebo, dulaglutide was also associated with a greater fall from baseline in 2-hydroxybutyric acid and a greater rise in threonine (P < 0.001). Increases from baseline in two of the proteins (but neither metabolite) were associated with MACE, including NT-proBNP (OR 1.267; 95% CI 1.119, 1.435; P < 0.001) and GDF-15 (OR 1.937; 95% CI 1.424, 2.634; P < 0.001). CONCLUSIONS: Dulaglutide was associated with a reduced 2-year rise from baseline of NT-proBNP and GDF-15. Higher rises of these biomarkers were also associated with MACE.

Semaglutide for cardiovascular event reduction in people with overweight or obesity: SELECT study baseline characteristics.

OBJECTIVE: This paper describes the baseline characteristics of the Semaglutide Effects on Heart Disease and Stroke in Patients with Overweight or Obesity (SELECT) study, one of the largest cardiovascular (CV) outcome studies in the field of obesity, which evaluates the effect of semaglutide versus placebo on major CV events. METHODS: SELECT enrolled individuals with overweight or obesity without diabetes, with prior myocardial infarction, stroke, and/or peripheral artery disease. This study reports participants' baseline characteristics in the full study population and subgroups defined by baseline glycated hemoglobin (HbA1c ; <5.7%, ≥5.7 to <6.0%, ≥6.0 to <6.5%), baseline waist to height ratio tertile, and qualifying prior CV event or condition. RESULTS: The study enrolled 17,605 participants (72.5% male) with an average (SD) age of 61.6 (8.9) years and BMI of 33.34 (5.04) kg/m2 . The most common prior CV event was myocardial infarction (76.3% of participants), followed by stroke (23.3%) and peripheral artery disease (8.6%). Furthermore, 24.3% had a heart failure diagnosis. Two-thirds of participants (66%) had HbA1c in the prediabetes range (5.7%-6.4%). Across groups of increasing HbA1c , prevalence of all CV risk factors increased. CONCLUSIONS: The enrolled population in SELECT includes participants across a broad range of relevant risk categories. This will allow the study to garner information about the CV benefits of semaglutide across these relevant clinical subgroups.

Relation of Incident Type 1 Diabetes to Recent COVID-19 Infection: Cohort Study Using e-Health Record Linkage in Scotland.

OBJECTIVE: Studies using claims databases reported that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection >30 days earlier was associated with an increase in the incidence of type 1 diabetes. Using exact dates of diabetes diagnosis from the national register in Scotland linked to virology laboratory data, we sought to replicate this finding. RESEARCH DESIGN AND METHODS: A cohort of 1,849,411 individuals aged <35 years without diabetes, including all those in Scotland who subsequently tested positive for SARS-CoV-2, was followed from 1 March 2020 to 22 November 2021. Incident type 1 diabetes was ascertained from the national registry. Using Cox regression, we tested the association of time-updated infection with incident diabetes. Trends in incidence of type 1 diabetes in the population from 2015 through 2021 were also estimated in a generalized additive model. RESULTS: There were 365,080 individuals who had at least one detected SARS-CoV-2 infection during follow-up and 1,074 who developed type 1 diabetes. The rate ratio for incident type 1 diabetes associated with first positive test for SARS-CoV-2 (reference category: no previous infection) was 0.86 (95% CI 0.62, 1.21) for infection >30 days earlier and 2.62 (95% CI 1.81, 3.78) for infection in the previous 30 days. However, negative and positive SARS-CoV-2 tests were more frequent in the days surrounding diabetes presentation. In those aged 0-14 years, incidence of type 1 diabetes during 2020-2021 was 20% higher than the 7-year average. CONCLUSIONS: Type 1 diabetes incidence in children increased during the pandemic. However, the cohort analysis suggests that SARS-CoV-2 infection itself was not the cause of this increase.

Cohort profile: the Scottish Diabetes Research Network national diabetes cohort - a population-based cohort of people with diabetes in Scotland.

PURPOSE: The Scottish Diabetes Research Network (SDRN)-diabetes research platform was established to combine disparate electronic health record data into research-ready linked datasets for diabetes research in Scotland. The resultant cohort, 'The SDRN-National Diabetes Dataset (SDRN-NDS)', has many uses, for example, understanding healthcare burden and socioeconomic trends in disease incidence and prevalence, observational pharmacoepidemiology studies and building prediction tools to support clinical decision making. PARTICIPANTS: We estimate that >99% of those diagnosed with diabetes nationwide are captured into the research platform. Between 2006 and mid-2020, the cohort comprised 472 648 people alive with diabetes at any point in whom there were 4 million person-years of follow-up. Of the cohort, 88.1% had type 2 diabetes, 8.8% type 1 diabetes and 3.1% had other types (eg, secondary diabetes). Data are captured from all key clinical encounters for diabetes-related care, including diabetes clinic, primary care and podiatry and comprise clinical history and measurements with linkage to blood results, microbiology, prescribed and dispensed drug and devices, retinopathy screening, outpatient, day case and inpatient episodes, birth outcomes, cancer registry, renal registry and causes of death. FINDINGS TO DATE: There have been >50 publications using the SDRN-NDS. Examples of recent key findings include analysis of the incidence and relative risks for COVID-19 infection, drug safety of insulin glargine and SGLT2 inhibitors, life expectancy estimates, evaluation of the impact of flash monitors on glycaemic control and diabetic ketoacidosis and time trend analysis showing that diabetic ketoacidosis (DKA) remains a major cause of death under age 50 years. The findings have been used to guide national diabetes strategy and influence national and international guidelines. FUTURE PLANS: The comprehensive SDRN-NDS will continue to be used in future studies of diabetes epidemiology in the Scottish population. It will continue to be updated at least annually, with new data sources linked as they become available.

Large socioeconomic gap in period life expectancy and life years spent with complications of diabetes in the Scottish population with type 1 diabetes, 2013-2018.

BACKGROUND: We report the first study to estimate the socioeconomic gap in period life expectancy (LE) and life years spent with and without complications in a national cohort of individuals with type 1 diabetes. METHODS: This retrospective cohort study used linked healthcare records from SCI-Diabetes, the population-based diabetes register of Scotland. We studied all individuals aged 50 and older with a diagnosis of type 1 diabetes who were alive and residing in Scotland on 1 January 2013 (N = 8591). We used the Scottish Index of Multiple Deprivation (SIMD) 2016 as an area-based measure of socioeconomic deprivation. For each individual, we constructed a history of transitions by capturing whether individuals developed retinopathy/maculopathy, cardiovascular disease, chronic kidney disease, and diabetic foot, or died throughout the study period, which lasted until 31 December 2018. Using parametric multistate survival models, we estimated total and state-specific LE at an attained age of 50. RESULTS: At age 50, remaining LE was 22.2 years (95% confidence interval (95% CI): 21.6 - 22.8) for males and 25.1 years (95% CI: 24.4 - 25.9) for females. Remaining LE at age 50 was around 8 years lower among the most deprived SIMD quintile when compared with the least deprived SIMD quintile: 18.7 years (95% CI: 17.5 - 19.9) vs. 26.3 years (95% CI: 24.5 - 28.1) among males, and 21.2 years (95% CI: 19.7 - 22.7) vs. 29.3 years (95% CI: 27.5 - 31.1) among females. The gap in life years spent without complications was around 5 years between the most and the least deprived SIMD quintile: 4.9 years (95% CI: 3.6 - 6.1) vs. 9.3 years (95% CI: 7.5 - 11.1) among males, and 5.3 years (95% CI: 3.7 - 6.9) vs. 10.3 years (95% CI: 8.3 - 12.3) among females. SIMD differences in transition rates decreased marginally when controlling for time-updated information on risk factors such as HbA1c, blood pressure, BMI, or smoking. CONCLUSIONS: In addition to societal interventions, tailored support to reduce the impact of diabetes is needed for individuals from low socioeconomic backgrounds, including access to innovations in management of diabetes and the prevention of complications.