Insulin resistance in type 1 diabetes: what is ‘double diabetes’ and what are the risks?

In this review, we explore the concept of ‘double diabetes’, a combination of type 1 diabetes with features of insulin resistance and type 2 diabetes. After considering whether double diabetes is a useful concept, we discuss potential mechanisms of increased insulin resistance in type 1 diabetes before examining the extent to which double diabetes might increase the risk of cardiovascular disease (CVD). We then go on to consider the proposal that weight gain from intensive insulin regimens may be associated with increased CV risk factors in some patients with type 1 diabetes, and explore the complex relationships between weight gain, insulin resistance, glycaemic control and CV outcome. Important comparisons and contrasts between type 1 diabetes and type 2 diabetes are highlighted in terms of hepatic fat, fat partitioning and lipid profile, and how these may differ between type 1 diabetic patients with and without double diabetes. In so doing, we hope this work will stimulate much-needed research in this area and an improvement in clinical practice

Flash Glucose-Sensing Technology as a Replacement for Blood Glucose Monitoring for the Management of Insulin-Treated Type 2 Diabetes: a Multicenter, Open-Label Randomized Controlled Trial

Introduction Glycemic control in participants with insulin-treated diabetes remains challenging. We assessed safety and efficacy of new flash glucose-sensing technology to replace self-monitoring of blood glucose (SMBG). Methods This open-label randomized controlled study (ClinicalTrials.gov, NCT02082184) enrolled adults with type 2 diabetes on intensive insulin therapy from 26 European diabetes centers. Following 2 weeks of blinded sensor wear, 2:1 (intervention/control) randomization (centrally, using biased-coin minimization dependant on study center and insulin administration) was to control (SMBG) or intervention (glucose-sensing technology). Participants and investigators were not masked to group allocation. Primary outcome was difference in HbA1c at 6 months in the full analysis set. Prespecified secondary outcomes included time in hypoglycemia, effect of age, and patient satisfaction. Results Participants (n = 224) were randomized (149 intervention, 75 controls). At 6 months, there was no difference in the change in HbA1c between intervention and controls: −3.1 ± 0.75 mmol/mol, [−0.29 ± 0.07% (mean ± SE)] and −3.4 ± 1.04 mmol/mol (−0.31 ± 0.09%) respectively; p = 0.8222. A difference was detected in participants aged <65 years [−5.7 ± 0.96 mmol/mol (−0.53 ± 0.09%) and −2.2 ± 1.31 mmol/mol (−0.20 ± 0.12%), respectively; p = 0.0301]. Time in hypoglycemia <3.9 mmol/L (70 mg/dL) reduced by 0.47 ± 0.13 h/day [mean ± SE (p = 0.0006)], and <3.1 mmol/L (55 mg/dL) reduced by 0.22 ± 0.07 h/day (p = 0.0014) for intervention participants compared with controls; reductions of 43% and 53%, respectively. SMBG frequency, similar at baseline, decreased in intervention participants from 3.8 ± 1.4 tests/day (mean ± SD) to 0.3 ± 0.7, remaining unchanged in controls. Treatment satisfaction was higher in intervention compared with controls (DTSQ 13.1 ± 0.50 (mean ± SE) and 9.0 ± 0.72, respectively; p < 0.0001). No serious adverse events or severe hypoglycemic events were reported related to sensor data use. Forty-two serious events [16 (10.7%) intervention participants, 12 (16.0%) controls] were not device-related. Six intervention participants reported nine adverse events for sensor-wear reactions (two severe, six moderate, one mild). Conclusion Flash glucose-sensing technology use in type 2 diabetes with intensive insulin therapy results in no difference in HbA1c change and reduced hypoglycemia, thus offering a safe, effective replacement for SMBG

Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk.

Variation in the human leukocyte antigen (HLA) genes accounts for one-half of the genetic risk in type 1 diabetes (T1D). Amino acid changes in the HLA-DR and HLA-DQ molecules mediate most of the risk, but extensive linkage disequilibrium complicates the localization of independent effects. Using 18,832 case-control samples, we localized the signal to 3 amino acid positions in HLA-DQ and HLA-DR. HLA-DQβ1 position 57 (previously known; P = 1 × 10(-1,355)) by itself explained 15.2% of the total phenotypic variance. Independent effects at HLA-DRβ1 positions 13 (P = 1 × 10(-721)) and 71 (P = 1 × 10(-95)) increased the proportion of variance explained to 26.9%. The three positions together explained 90% of the phenotypic variance in the HLA-DRB1-HLA-DQA1-HLA-DQB1 locus. Additionally, we observed significant interactions for 11 of 21 pairs of common HLA-DRB1-HLA-DQA1-HLA-DQB1 haplotypes (P = 1.6 × 10(-64)). HLA-DRβ1 positions 13 and 71 implicate the P4 pocket in the antigen-binding groove, thus pointing to another critical protein structure for T1D risk, in addition to the HLA-DQ P9 pocket.This research utilizes resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. This work is supported in part by funding from the National Institutes of Health (5R01AR062886-02 (PIdB), 1R01AR063759 (SR), 5U01GM092691-05 (SR), 1UH2AR067677-01 (SR), R01AR065183 (PIWdB)), a Doris Duke Clinical Scientist Development Award (SR), the Wellcome Trust (JAT) and the National Institute for Health Research (JAT and JMMH), and a Vernieuwingsimpuls VIDI Award (016.126.354) from the Netherlands Organization for Scientific Research (PIWdB). TLL was supported by the German Research Foundation (LE 2593/1-1 and LE 2593/2-1).This is the accepted manuscript. The final version is available at http://www.nature.com/ng/journal/v47/n8/full/ng.3353.html

Endothelial Cells in Co-culture Enhance Embryonic Stem Cell Differentiation to Pancreatic Progenitors and Insulin-Producing Cells through BMP Signaling

Endothelial cells (ECs) represent the major component of the embryonic pancreatic niche and play a key role in the differentiation of insulin-producing β cells in vivo. However, it is unknown if ECs promote such differentiation in vitro. We investigated whether interaction of ECs with mouse embryoid bodies (EBs) in culture promotes differentiation of pancreatic progenitors and insulin-producing cells and the mechanisms involved. We developed a co-culture system of mouse EBs and human microvascular ECs (HMECs). An increase in the expression of the pancreatic markers PDX-1, Ngn3, Nkx6.1, proinsulin, GLUT-2, and Ptf1a was observed at the interface between EBs and ECs (EB-EC). No expression of these markers was found at the periphery of EBs cultured without ECs or those co-cultured with mouse embryonic fibroblasts (MEFs). At EB-EC interface, proinsulin and Nkx6.1 positive cells co-expressed phospho-Smad1/5/8 (pSmad1/5/8). Therefore, EBs were treated with HMEC conditioned media (HMEC-CM) suspecting soluble factors involved in bone morphogenetic protein (BMP) pathway activation. Upregulation of PDX-1, Ngn3, Nkx6.1, insulin-1, insulin-2, amylin, SUR1, GKS, and amylase as well as down-regulation of SST were detected in treated EBs. In addition, higher expression of BMP-2/-4 and their receptor (BMPR1A) were also found in these EBs. Recombinant human BMP-2 (rhBMP-2) mimicked the effects of the HMEC-CM on EBs. Noggin (NOG), a BMP antagonist, partially inhibited these effects. These results indicate that the differentiation of EBs to pancreatic progenitors and insulin-producing cells can be enhanced by ECs in vitro and that BMP pathway activation is central to this process

Low Adiponectin Levels Are an Independent Predictor of Mixed and Non-Calcified Coronary Atherosclerotic Plaques

Atherosclerosis is the primary cause of coronary artery disease (CAD). There is increasing recognition that lesion composition rather than size determines the acute complications of atherosclerotic disease. Low serum adiponectin levels were reported to be associated with coronary artery disease and future incidence of acute coronary syndrome (ACS). The impact of adiponectin on lesion composition still remains to be determined. We measured serum adiponectin levels in 303 patients with stable typical or atypical chest pain, who underwent dual-source multi-slice CT-angiography to exclude coronary artery stenosis. Atherosclerotic plaques were classified as calcified, mixed or non-calcified. In bivariate analysis adiponectin levels were inversely correlated with total coronary plaque burden (r = -0.21, p = 0.0004), mixed (r = -0.20, p = 0.0007) and non-calcified plaques (r = -0.18, p = 0.003). No correlation was seen with calcified plaques (r = -0.05, p = 0.39). In a fully adjusted multivariate model adiponectin levels remained predictive of total plaque burden (estimate: -0.036, 95%CI: -0.052 to -0.020, p<0.0001), mixed (estimate: -0.087, 95%CI: -0.132 to -0.042, p = 0.0001) and non-calcified plaques (estimate: -0.076, 95%CI: -0.115 to -0.038, p = 0.0001). Adiponectin levels were not associated with calcified plaques (estimate: -0.021, 95% CI: -0.043 to -0.001, p = 0.06). Since the majority of coronary plaques was calcified, adiponectin levels account for only 3% of the variability in total plaque number. In contrast, adiponectin accounts for approximately 20% of the variability in mixed and non-calcified plaque burden. Adiponectin levels predict mixed and non-calcified coronary atherosclerotic plaque burden. Low adiponectin levels may contribute to coronary plaque vulnerability and may thus play a role in the pathophysiology of ACS