Efficacy and safety of alirocumab in insulin-treated patients with type 1 or type 2 diabetes and high cardiovascular risk:Rationale and design of the ODYSSEY DM-INSULIN trial

Aims: The coadministration of alirocumab, a PCSK9 inhibitor for treatment of hypercholesterolaemia, and insulin in diabetes mellitus (DM) requires further study. Described here is the rationale behind a phase-IIIb study designed to characterize the efficacy and safety of alirocumab in insulin-treated patients with type 1 (T1) or type 2 (T2) DM with hypercholesterolaemia and high cardiovascular (CV) risk. Methods: ODYSSEY DM-INSULIN (NCT02585778) is a randomized, double-blind, placebo-controlled, multicentre study that planned to enrol around 400 T2 and up to 100 T1 insulin-treated DM patients. Participants had low-density lipoprotein cholesterol (LDL-C) levels at screening. ≥. 70. mg/dL (1.81. mmol/L) with stable maximum tolerated statin therapy or were statin-intolerant, and taking (or not) other lipid-lowering therapy; they also had established CV disease or at least one additional CV risk factor. Eligible patients were randomized 2:1 to 24. weeks of alirocumab 75. mg every 2. weeks (Q2W) or a placebo. Alirocumab-treated patients with LDL-C. ≥. 70. mg/dL at week 8 underwent a blinded dose increase to 150. mg Q2W at week 12. Primary endpoints were the difference between treatment arms in percentage change of calculated LDL-C from baseline to week 24, and alirocumab safety. Results: This is an ongoing clinical trial, with 76 T1 and 441 T2 DM patients enrolled; results are expected in mid-2017. Conclusion: The ODYSSEY DM-INSULIN study will provide information on the efficacy and safety of alirocumab in insulin-treated individuals with T1 or T2 DM who are at high CV risk and have hypercholesterolaemia not adequately controlled by the maximum tolerated statin therapy

The cost of prevalent and incident cardiovascular disease in people with type 2 diabetes in Scotland: data from the Scottish Care Information–Diabetes Collaboration

Aim To compare costs for three groups of people with type 2 diabetes, those at high risk of future cardiovascular disease, those without cardiovascular disease and those with established cardiovascular disease, and to also compare costs incurred by people with type 2 diabetes with an incident cardiovascular disease event with those who remain incident event‐free over a 3‐year period. Methods Data about people with type 2 diabetes in Scotland were obtained from the Scottish Care Information Diabetes registry. Data linkage was used to retrieve information on healthcare utilization, care home use and deaths. Productivity effects were estimated for those of non‐pensionable age. We estimated costs over 12 months (prevalent cardiovascular disease) and 3 years from incident cardiovascular disease event. Results Mean annual cost per person with established cardiovascular disease was £6900, £3300 for a person at high risk of future cardiovascular disease, and £2500 for a person without cardiovascular disease and not at high risk. In year 1, the cost of an incident cardiovascular disease event was £16 700 compared with £2100 for people without an incident event. Over 2 years, the cumulative costs were £21 500 and £4200, and by year 3, £25 000 and £5900, respectively. Conclusions Cardiovascular disease in people with type 2 diabetes places a significant financial burden on healthcare and the wider economy. Our results emphasize the financial consequences of cardiovascular disease prevention strategies

A gene variant near ATM is significantly associated with metformin treatment response In type 2 diabetes: A replication and meta-analysis of five cohorts

_Aims/hypothesis:_ In this study we aimed to replicate the previously reported association between the glycaemic response to metformin and the SNP rs11212617 at a locus that includes the ataxia telangiectasia mutated (ATM) gene in multiple additional populations. _Methods:_ Incident users of metformin selected from the Diabetes Care System West-Friesland (DCS, n=929) and the Rotterdam Study (n=182) from the Netherlands, and the CARDS Trial (n=254) from the UK were genotyped for rs11212617 and tested for an association with both HbA1c reduction and treatment success, defined as the ability to reach the treatment target of an HbA1c ≤7 % (53 mmol/mol). Finally, a meta-analysis including data from literature was performed. _Results:_ In the DCS cohort, we observed an association between rs11212617 genotype and treatment success on metformin (OR 1.27, 95% CI 1.03, 1.58, p=0.028); in the smaller Rotterdam Study cohort, a numerically similar but non-significant trend was observed (OR 1.45, 95% CI 0.87, 2.39, p=0.15); while in the CARDS cohort there was no significant association. In meta-analyses of these three cohorts separately or combined with the previously published cohorts, rs11212617 genotype is associated with metformin treatment success (OR 1.24, 95% CI 1.04, 1.49, p=0.016 and OR 1.25, 95% CI 1.33, 1.38, p=7.8×10-6, respectively). _ Conclusions/inte

Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases

Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation (N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases.Molecular Epidemiolog

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

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes

Aims/hypothesis We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes. Methods In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA(1c). Conclusions/interpretation We identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed