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OBJECTIVE To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c. RESEARCH DESIGN AND METHODS We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups. RESULTS Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Aims/hypothesis The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes. Methods Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA1c, C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data. Results Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10–15), without evidence for reverse causality. Conclusions/interpretation Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits. Graphical abstract

OBJECTIVE To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c. RESEARCH DESIGN AND METHODS We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five RHAPSODY data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide and HDL) and four risk-driven subgroups using fixed cut-offs for HbA1c and risk of cardiovascular disease based on guidelines. The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist’s subgroups. RESULTS Under care-as-usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost 22.0% and 25.3% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to ten-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Aims/hypothesis Approximately 25% of people with type 2 diabetes experience a foot ulcer and their risk of amputation is 10–20 times higher than that of people without type 2 diabetes. Prognostic models can aid in targeted monitoring but an overview of their performance is lacking. This study aimed to systematically review prognostic models for the risk of foot ulcer or amputation and quantify their predictive performance in an independent cohort. Methods A systematic review identified studies developing prognostic models for foot ulcer or amputation over minimal 1 year follow-up applicable to people with type 2 diabetes. After data extraction and risk of bias assessment (both in duplicate), selected models were externally validated in a prospective cohort with a 5 year follow-up in terms of discrimination (C statistics) and calibration (calibration plots). Results We identified 21 studies with 34 models predicting polyneuropathy, foot ulcer or amputation. Eleven models were validated in 7624 participants, of whom 485 developed an ulcer and 70 underwent amputation. The models for foot ulcer showed C statistics (95% CI) ranging from 0.54 (0.54, 0.54) to 0.81 (0.75, 0.86) and models for amputation showed C statistics (95% CI) ranging from 0.63 (0.55, 0.71) to 0.86 (0.78, 0.94). Most models underestimated the ulcer or amputation risk in the highest risk quintiles. Three models performed well to predict a combined endpoint of amputation and foot ulcer (C statistics >0.75). Conclusions/interpretation Thirty-four prognostic models for the risk of foot ulcer or amputation were identified. Although the performance of the models varied considerably, three models performed well to predict foot ulcer or amputation and may be applicable to clinical practice. Graphical abstract

Type 2 diabetes is a complex, multifactorial disease with varying presentation and underlying pathophysiology. Recent studies using data-driven cluster analysis have led to a stratification of type 2 diabetes into novel subgroups based on six clinical measurements. Whether these subgroups truly correspond to the underlying phenotypic differences is nevertheless unclear. Here, we apply an unsupervised, data-driven clustering method (Similarity Network Fusion) to characterize type 2 diabetes in two independent cohorts involving 1,134 subjects in total based on integrated plasma lipidomics and peptidomics data without pre-selection. Logistic regression was then used to explore clustering based on ≥ 180 circulating lipids and 1,195 protein biomarkers, alongside clinical signatures. Two subgroups were identified, one of which associated with elevated C-peptide levels, diabetic complications and more severe insulin resistance compared to the other. GWAS analysis against 403 type 2 diabetes risk variants revealed associations of several SNPs with clusters and altered molecular profiles. We thus demonstrate that heterogeneity in type 2 diabetes can be captured by circulating omics alone using an unsupervised bottom-up approach. Such multiomics signatures could reflect pathological mechanisms underlying type 2 diabetes and thus may help inform on precision medicine approaches to disease management.

Objective We investigated whether serum magnesium (Mg2+) was prospectively associated with macro- or microvascular complications and mediated by glycemic control (Hemoglobin A1c (HbA1c)), in T2D. Research Design and Methods We analyzed in 4,348 participants the association of serum Mg2+ with macrovascular disease and mortality (acute myocardial infarction (AMI), coronary heart disease (CHD), heart failure (HF), cerebrovascular accident (CVA), peripheral arterial disease (PAD)), atrial fibrillation (AF) and microvascular complications (chronic kidney disease (CKD), diabetic retinopathy and diabetic foot) using Cox regression, adjusted for confounders. Mediation analysis was performed to assess whether HbA1c mediated these associations. Results The average baseline serum Mg2+ concentration was 0.80 ± 0.08 mmol/L. Serum Mg2+ was during 6.1 years of follow-up inversely associated with major macrovascular 0.87 (95% CI: 0.76; 1.00), HF 0.76 (95% CI: 0.62; 0.93) and AF 0.59 (95% CI: 0.49; 0.72). Serum Mg2+ was not associated with AMI, CHD, CVA and PAD. Serum Mg2+ was during 5.1 years of follow-up inversely associated with overall microvascular events 0.85 (95% CI: 0.78; 0.91), 0.89 (95% CI: 0.82; 0.96) for CKD, 0.77 (95% CI: 0.61; 0.98) for diabetic retinopathy and 0.85 (95% CI: 0.78; 0.92) for diabetic foot. HbA1c mediated the associations of serum Mg2+ with HF, overall microvascular events, diabetic retinopathy and diabetic foot. Conclusions Serum Mg2+ concentration is inversely associated with the risk to develop HF, AF and with the occurrence of CKD, diabetic retinopathy and foot complications, in T2D. Glycemic control partially mediated the association of serum Mg2+ with HF and microvascular complications.

Loss of the tumor suppressor gene CDKN2A, encoding p16 and p14, is a frequent event driving melanoma progression. Therefore, therapeutic strategies aimed at CDKN2A loss hold great potential to improve melanoma treatment. Pharmacological inhibition of the p16 targets CDK4/6 is a prime example of such a strategy. Other approaches exploit cell cycle deregulation, target metabolic rewiring, epigenetically restore expression, act on dependencies resulting from co-deleted genes, or are directed at the effects of CDKN2A loss on immune responses. This review explores these therapeutic strategies targeting CDKN2A loss, which potentially open up new avenues for precision medicine in melanoma.

Aims/hypothesisInflammation is important in development of type 2 diabetes complications. The N-glycosylation of IgG influences its role in inflammation. Until now, the association of IgG N-glycosylation with type 2 diabetes complications has not been extensively investigated. We hypothesized that N-glycosylation of IgG may be related to development of complications of type 2 diabetes.MethodsIn three independent type 2 diabetes cohorts, IgG N-glycosylation was measured by UPLC (DiaGene n=1815, GenodiabMar n=640) and mass spectrometry (DCS n=1266). We investigated the associations of IgG N-glycosylation (fucosylation, galactosylation, sialylation and bisection) with incident and prevalent nephropathy, retinopathy and macrovascular disease using Cox- and logistic regression, followed by meta-analyses. The models were adjusted for age, sex and additionally for clinical risk factors.ResultsIgG galactosylation was negatively associated with prevalent and incident nephropathy after adjustment for clinical risk factors. Sialylation was negatively associated with incident diabetic nephropathy. For retinopathy, similar associations were found for galactosylation in the basic model. For macrovascular complications, negative associations with galactosylation and sialylation were confined to the cross-sectional analyses.ConclusionsWe showed that IgG N-glycosylation traits are associated with higher prevalence and future development of nephropathy, after correction for clinical risk factors. For other complications, IgG N-glycosylation was associated with their prevalence only, possibly reflecting ongoing vascular inflammation. These findings indicate the predictive potential of IgG N-glycosylation in nephropathy.

Objective: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However there exists significant variability in glycaemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction. Research design and methods: As an initiative of the Metformin Genetics Plus (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortia, 5,485 white Europeans with type 2 diabetes treated with sulfonylurea were recruited from 6 referral centres in Europe and North America. We first estimated heritability using generalized restricted maximum likelihood (REML) and then undertook GWAS of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis-eQTLs and functional validation in cellular models. Finally, we examined for a possible drug-drug-gene interactions. Results: After establishing that sulfonylurea response is heritable (37±11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction at a genome-wide scale (p < 5×10-8). The C-allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), p=2.39×10−8) lower reduction in HbA1c. Similarly, the C-allele was associated with higher glucose trough levels (β=1.61, p=0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). In 3, 029 human whole blood samples, the C-allele is a cis-eQTL for increased expression of GXYLT1 (β=0.21, p=2.04×10-58). The C-allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA1c (p=4.80×10−8). In 1,183 human liver samples, the C-allele at rs10770791 is a cis-eQTL for reduced SLCO1B1 expression (p=1.61×10−7) which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use, sulfonylurea response and SCLO1B1 genotype was observed (p=0.001). In statin non-users, C-allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48±0.12% (5.2±1.26 mmol/mol)), equivalent to initiating a DPP4 inhibitor. Conclusion: We have identified clinically important genetic effects at genome wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important when prescribing glucose-lowering drugs.

Reactivation of dormant cancer cells can lead to cancer relapse, metastasis and patient death. Dormancy is a non-proliferative state and is linked to late relapse and death. No targeted therapy is currently available to eliminate dormant cells, highlighting the need for a deeper understanding and reliable models. Here, we thoroughly characterize the dormant D2.OR and proliferative D2A1 breast cancer cell line models in vivo and in vitro, and assess if there is overlap between a dormant and a senescent phenotype. We show that D2.OR but not D2A1 cells become dormant in the liver of an immunocompetent model. In vitro, we show that D2.OR cells are polyploid ER+/Her2+ cells, and in response to a 3D environment are growth arrested in G1, of which a subpopulation resides in a 4NG1 state. The dormancy state is reversible, and not associated with a senescence phenotype. This will aid future research on breast cancer dormancy.

IntroductionAlthough associations of total plasma N-glycome (TPNG) with type 2 diabetes have been reported, little is known on the role of TPNG in type 2 diabetes complications, a major cause of type 2 diabetes-related morbidity and mortality. Here, we assessed TPNG in relation to type 2 diabetes complications in subsamples of two Dutch cohorts using mass spectrometry (n=1815 in DiaGene and n=1518 in Hoorn Diabetes Care System).Research design and methodsBlood plasma samples and technical replicates were pipetted into 96-well plates in a randomized manner. Peptide:N-glycosidase F (PNGase F) was used to release N-glycans, whereafter sialic acids were derivatized for stabilization and linkage differentiation. After total area normalization, 68 individual glycan compositions were quantified in total and were used to calculate 45 derived traits which reflect structural features of glycosylation. Associations of glycan features with prevalent and incident microvascular or macrovascular complications were tested in logistic and Cox regression in both independent cohorts and the results were meta-analyzed.ResultsOur results demonstrated similarities between incident and prevalent complications. The strongest association for prevalent cardiovascular disease was a high level of bisection on a group of diantennary glycans (A2FS0B; OR=1.38, p=1.34×10−11), while for prevalent nephropathy the increase in 2,6-sialylation on triantennary glycans was most pronounced (A3E; OR=1.28, p=9.70×10−6). Several other TPNG features, including fucosylation, galactosylation, and sialylation, firmly demonstrated associations with prevalent and incident complications of type 2 diabetes.ConclusionsThese findings may provide a glance on how TPNG patterns change before complications emerge, paving the way for future studies on prediction biomarkers and potentially disease mechanisms.

Type 1 diabetes is an autoimmune disease characterized by autoreactive T-cell mediated destruction of the insulin-producing pancreatic beta-cells. Increasing evidence suggest that the beta-cells themselves contribute to their own destruction by generating neo-antigens through the production of aberrant or modified proteins that escape central tolerance. We have recently demonstrated that ribosomal infidelity amplified by stress could lead to the generation of neoantigens in human beta-cells, emphasizing the participation of nonconventional translation events to autoimmunity, as occurring in cancer or virus-infected tissues. Using a transcriptome-wide profiling approach to map translation initiation start sites in human beta-cells under standard and inflammatory conditions, we identify a completely new set of polypeptides derived from non-canonical start sites and translation initiation within lncRNA. Our data underline the extreme diversity of the beta-cell translatome and may reveal new functional biomarkers for beta-cell distress, disease prediction and progression and therapeutic intervention in type 1 diabetes.

Objectives To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. Design Systematic review and external validation. Data sources PubMed and Embase. Eligibility criteria Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. Methods Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell’s C statistic). Results 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. Conclusions This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. Systematic review registration PROSPERO CRD42020192831.

Type 1 diabetes is an autoimmune disease characterized by autoreactive T-cell mediated destruction of the insulin-producing pancreatic beta-cells. Increasing evidence suggest that the beta-cells themselves contribute to their own destruction by generating neo-antigens through the production of aberrant or modified proteins that escape central tolerance. We have recently demonstrated that ribosomal infidelity amplified by stress could lead to the generation of neoantigens in human beta-cells, emphasizing the participation of nonconventional translation events to autoimmunity, as occurring in cancer or virus-infected tissues. Using a transcriptome-wide profiling approach to map translation initiation start sites in human beta-cells under standard and inflammatory conditions, we identify a completely new set of polypeptides derived from non-canonical start sites and translation initiation within lncRNA. Our data underline the extreme diversity of the beta-cell translatome and may reveal new functional biomarkers for beta-cell distress, disease prediction and progression and therapeutic intervention in type 1 diabetes.

Ovo-like transcriptional repressor 1 (OVOL1) is a key mediator of epithelial lineage determination and mesenchymal–epithelial transition (MET). The cytokines transforming growth factor-β (TGF-β) and bone morphogenetic proteins (BMP) control the epithelial–mesenchymal plasticity (EMP) of cancer cells, but whether this occurs through interplay with OVOL1 is not known. Here, we show that OVOL1 is inversely correlated with the epithelial–mesenchymal transition (EMT) signature, and is an indicator of a favorable prognosis for breast cancer patients. OVOL1 suppresses EMT, migration, extravasation, and early metastatic events of breast cancer cells. Importantly, BMP strongly promotes the expression of OVOL1, which enhances BMP signaling in turn. This positive feedback loop is established through the inhibition of TGF-β receptor signaling by OVOL1. Mechanistically, OVOL1 interacts with and prevents the ubiquitination and degradation of SMAD family member 7 (SMAD7), which is a negative regulator of TGF-β type I receptor stability. Moreover, a small-molecule compound 6-formylindolo(3,2-b)carbazole (FICZ) was identified to activate OVOL1 expression and thereby antagonizing (at least in part) TGF-β-mediated EMT and migration in breast cancer cells. Our results uncover a novel mechanism by which OVOL1 attenuates TGF-β/SMAD signaling and maintains the epithelial identity of breast cancer cells.

We have deployed a multi-omics approach in large cohorts of patients with existing type 2 diabetes to identify biomarkers for disease progression across three molecular classes, metabolites, lipids and proteins. A Cox regression analysis for association with time to insulin requirement in 2,973 patients in the DCS, ANDIS and GoDARTS cohorts identified homocitrulline, isoleucine and 2-aminoadipic acid, as well as the bile acids glycocholic and taurocholic acids, as predictive of more rapid deterioration. Increased levels of eight triacylglycerol species, and lowered levels of the sphingomyelin SM 42:2;2 were also predictive of disease progression. Of ∼1,300 proteins examined in two cohorts, levels of GDF-15/MIC1, IL-18RA, CRELD1, NogoR, FAS, and ENPP7 were associated with faster progression, whilst SMAC/DIABLO, COTL1, SPOCK1 and HEMK2 predicted lower progression rates. Strikingly, identified proteins and lipids were also associated with diabetes incidence and prevalence in external replication cohorts. Implicating roles in disease compensation, NogoR/RTN4R improved glucose tolerance in high fat-fed mice and tended to improved insulin signalling in liver cells whilst IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. Conversely, high NogoR levels led to islet cell apoptosis. This comprehensive, multi-disciplinary approach thus identifies novel biomarkers with potential prognostic utility, provides evidence for new disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

© 2020 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UKAim: To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. Methods: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. Results: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1–4. Participants in Subgroups 2–4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13–0.58), Subgroup 3 (β = 0.30; 95% CI 0.10–0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04–0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. Conclusions: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

Epigenetic programming is essential for lineage differentiation, embryogenesis and placentation in early pregnancy. In epigenetic association studies, DNA methylation is often examined in DNA derived from white blood cells, although its validity to other tissues of interest remains questionable. Therefore, we investigated the tissue specificity of epigenome-wide DNA methylation in newborn and placental tissues. Umbilical cord white blood cells (UC-WBC, n = 25), umbilical cord blood mononuclear cells (UC-MNC, n = 10), human umbilical vein endothelial cells (HUVEC, n = 25) and placental tissue (n = 25) were obtained from 36 uncomplicated pregnancies. Genome-wide DNA methylation was measured by the Illumina HumanMethylation450K BeadChip. Using UC-WBC as a reference tissue, we identified 3595 HUVEC tissue-specific differentially methylated regions (tDMRs) and 11,938 placental tDMRs. Functional enrichment analysis showed that HUVEC and placental tDMRs were involved in embryogenesis, vascular development and regulation of gene expression. No tDMRs were identified in UC-MNC. In conclusion, the extensive amount of genome-wide HUVEC and placental tDMRs underlines the relevance of tissue-specific approaches in future epigenetic association studies, or the use of validated representative tissues for a certain disease of interest, if available. To this purpose, we herewith provide a relevant dataset of paired, tissue-specific, genome-wide methylation measurements in newborn tissues.

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Background: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However there exists significant variability in glycaemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction. Methods: As an initiative of the Metformin Genetics Plus (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortia, 5,485 white European adults with type 2 diabetes treated with sulfonylurea were recruited from 6 referral centres in Europe and North America. We first estimated heritability using generalized restricted maximum likelihood (REML) and then undertook GWAS of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy in each cohort using linear regression followed by meta-analysis. These results were supported by cis-eQTLs and functional validation in cellular models. Findings: After establishing that sulfonylurea response is heritable (37±11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction. The C-allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), p=2.4×10−8) lower HbA1c reduction. Similarly, the C-allele was associated with higher glucose trough levels (β=1.61, p=0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). The C-allele of rs10770791, near SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater HbA1c reduction (p=4.8×10 −8 ). In 1,183 human liver samples, the C-allele at rs10770791 is a cis-eQTL for reduced SLCO1B1 expression (p=1.61×10−7) which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use, sulfonylurea response and SCLO1B1 genotype was observed (p=0.001). In statin non-users (but not users), C-allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48±0.12% (5.2±1.26 mmol/mol)), equivalent to initiating a DPP4 inhibitor. Interpretation: We have identified clinically important genetic effects at genomewide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important when prescribing glucose-lowering drugs. Funding Statement: Innovative Medicines Initiative, the National Institutes of Health, the Geisinger Health Plan Quality Pilot Fund. Declaration of Interests: ERP has received honoraria for speaking from Lilly and Sanofi. JCF has received honoraria for speaking at scientific conferences from Novo, and for consulting from Goldfinch Bio. All other authors declare no competing interest. Ethics Approval Statement: This study was approved by respective research ethics review boards and participants provided written informed consent.

AIMS: The United Kingdom Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2) is a health economic decision model supporting long-term evaluation of treatment options in diabetes. The aim of the study was to externally validate the model by comparing the predicted and observed outcomes in two European population-based cohorts of people with type 2 diabetes. MATERIALS AND METHODS: We used data from the Casale Monferrato Survey (CMS) [n=1931] and a subgroup of the Hoorn Diabetes Care System (DCS) cohorts [N=5188]. The following outcomes were analysed: all-cause mortality, myocardial infarction (MI), ischaemic heart disease (IHD), stroke, and congestive heart failure (CHF). Model performance was assessed by comparing predictions with observed cumulative incidences in each cohort during follow-up. RESULTS: All-cause mortality was overestimated by the UKPDS-OM2 in both the cohorts, with a bias of 0.05 in CMS and 0.12 in DCS at 10 years of follow-up. For MI, predictions were consistently higher than observed incidence over the entire follow-up in both cohorts (10 years bias 0.07 for CMS and 0.10 for DCS). The model performed well for stroke and IHD outcomes in both cohorts. Heart failure incidence was predicted well for DCS (5 years bias -0.001) but underestimated for CMS. CONCLUSIONS: UKPDS-OM2 consistently overpredicted the risk of mortality and MI in both cohorts during follow-up. Period effects may partially explain the differences. Results indicate that transferability is not satisfactory for all outcomes and new or adjusted risk equations may be needed before applying the model to the Italian or Dutch settings. This article is protected by copyright. All rights reserved.

Aims/hypothesisFive clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes (T2D). In the current study we replicate and cross-validate these T2D clusters in three large cohorts using readily measured variables in the clinic.MethodsIn this cross-sectional study, 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide and HDL in three independent cohorts. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres.ResultsFive distinct T2D clusters were identified and mapped back to the original four ANDIS clusters. Using C-peptide and HDL instead of HOMA-B and HOMA-S three of the clusters mapped with high sensitivity (80.6 – 90.7%) to the previously identified Severe Insulin Deficient (SIDD), Severe insulin resistant (SIRD) and Obese (MOD) clusters. The previously described ANDIS MARD cluster could be mapped to the two milder groups in our study – one characterised by high HDL, and the other having not any extreme characteristic (MDH cluster). When these two milder groups were combined they mapped well to the previously labelled MARD cluster (sensitivity 79.4%). In the cross-validation between cohorts, particularly the SIDD and MDH cluster cross-validated well with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity ranging from 36.1% to 92.3% where individuals shifted from SIRD to MD and vice versa.Conclusions/interpretationClusters based on C-peptide instead of HOMA measures result in clusters that resemble those based on HOMA measures, especially for SIDD, SIRD and MOD. By adding HDL, the MARD cluster based upon HOMA measures resulted in the current clustering in two clusters with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts.

BackgroundThe rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D.MethodsClusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts.ResultsWe identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling.ConclusionsOur results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

SummarydsSwissKnife is an R package that enables several powerful analyses to be performed on federated datasets. The package works alongside DataSHIELD and extends its functionality. We have developed and implemented dsSwissKnife in a large IMI project on type 2 diabetes, RHAPSODY, where data from 10 observational cohorts have been harmonised and federated in CDISC SDTM format and made available for biomarker discovery.Availability and implementationdsSwissKnife is freely available online at https://github.com/sib-swiss/dsSwissKnife. The package is distributed under the GNU General Public License version 3.Contactmark.ibberson@sib.swiss

Prenatal exposure to infectious and/or inflammatory insults is increasingly recognized to contribute to the etiology of psychiatric disorders with neurodevelopmental components. Recent research using animal models suggests that maternal immune activation (MIA) can induce transgenerational effects on brain and behavior, possibly through epigenetic mechanisms. Using a mouse model of MIA that is based on gestational treatment with the viral mimeticpoly(I:C) (= polyriboinosinic-polyribocytidilic acid), the present study explored whether the transgenerational effects of MIA are extendable to dopaminergic dysfunctions. We show that the direct descendants born to poly(I:C)-treated mothers display signs of hyperdopaminergia, as manifested by a potentiated sensitivity to the locomotor-stimulating effects of amphetamine (Amph) and increased expression of tyrosine hydroxylase (Th) in the adult ventral midbrain. In stark contrast, second- and third-generation offspring of MIA-exposed ancestors displayed blunted locomotor responses to Amph and reduced expression of Th. Furthermore, we found increased DNA methylation at the promoter region of the dopamine-specifying factor, nuclear receptor-related 1 protein (Nurr1), in the sperm of first-generation MIA offspring and in the ventral midbrain of second-generation offspring of MIA-exposed ancestors. The latter effect was further accompanied by reduced mRNA levels of Nurr1 in this brain region. Together, our results suggest that MIA has the potential to modify dopaminergic functions across multiple generations with opposite effects in the direct descendants and their progeny. The presence of altered DNA methylation in the sperm of MIA-exposed offspring highlights the possibility that epigenetic processes in the male germline play a role in the transgenerational effects of MIA., Neuropsychopharmacology, 46 (2), ISSN:0893-133X, ISSN:1740-634X