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Aim/hypothesis Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes. Methods We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205). Results Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy. Conclusions /interpretation Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies. Graphical abstract

Type 2 diabetes (T2D) is defined by a single metabolite, glucose, but is increasingly recognized as a highly heterogeneous disease, including individuals with varying clinical characteristics, disease progression, drug response, and risk of complications. Identification of subtypes with differing risk profiles and disease etiologies at diagnosis could open up avenues for personalized medicine and allow clinical resources to be focused to the patients who would be most likely to develop diabetic complications, thereby both improving patient health and reducing costs for the health sector. More homogeneous populations also offer increased power in experimental, genetic, and clinical studies. Clinical parameters are easily available and reflect relevant disease pathways, including the effects of both genetic and environmental exposures. We used six clinical parameters (GAD autoantibodies, age at diabetes onset, HbA1c, BMI, and measures of insulin resistance and insulin secretion) to cluster adult-onset diabetes patients into five subtypes. These subtypes have been robustly reproduced in several populations and associated with different risks of complications, comorbidities, genetics, and response to treatment. Importantly, the group with severe insulin-deficient diabetes (SIDD) had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes (SIRD) group had the highest risk for diabetic kidney disease (DKD) and fatty liver, emphasizing the importance of insulin resistance for DKD and hepatosteatosis in T2D. In conclusion, we believe that subclassification using these highly relevant parameters could provide a framework for personalized medicine in diabetes.

ObjectiveHuman traits are heritable, and some of these including metabolic and lipid phenotypes show preferential parental transmissions, or parent-of-origin effects. These have been mostly studied in populations comprising adults. We aimed to investigate heritability and parent-of-origin effects on cardiometabolic and anthropometric traits in a birth-cohort with serial measurements to assess if these effects manifested at an early age.Research design and methodsWe investigated heritability and parent-of-origin effects on cardiometabolic and anthropometric traits in the Pune Maternal Nutrition Study (PMNS) wherein offspring and parents were studied from birth and followed-up for 18 years. Heritability was estimated by calculating association between mid-parental phenotypes and offspring. Maternal and paternal effects on offspring phenotype were modelled by regression after adjusting for age, sex and BMI. Parent-of-origin effects were calculated by the difference between maternal and paternal effects.ResultsAnthropomorphic traits and cardiometabolic traits were robustly heritable. Parent-of-origin effects were observed for glycemic traits at both 6- and 12-years, with a paternal effect at 6-years which transitioned to a maternal effect at 12-years. For insulin and HOMA-S, a negative maternal effect transitioned to a positive one at 12-years. For HOMA-B, a paternal effect at 6-years transitioned to a maternal one at 12-years. Lipid traits consistently showed stronger maternal influence while anthropometric traits did not show any parental biases.ConclusionsOur study highlights that parental programming of cardiometabolic traits is evident from early childhood and can transition during puberty. Further studies are needed to determine the mechanisms of underlying such effects.

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy (GenDIP) Consortium assembled genome-wide association studies (GWAS) of diverse ancestry in a total of 5,485 women with GDM and 347,856 without GDM. Through trans-ancestry meta-analysis, we identified five loci with genome-wide significant association (p−8) with GDM, mapping to/near MTNR1B (p=4.3×10−54), TCF7L2 (p=4.0×10−16), CDKAL1 (p=1.6×10−14), CDKN2A-CDKN2B (p=4.1×10−9) and HKDC1 (p=2.9×10−8). Multiple lines of evidence pointed to genetic contributions to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D; and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomisation analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

PURPOSE: Intrauterine undernutrition is associated with increased risk of type 2 diabetes. Children born premature or small for gestational age were reported to have abnormal retinal vascularization. However, whether intrauterine famine act as a trigger for diabetes complications, including retinopathy, is unknown. The aim of the current study was to evaluate long-term effects of perinatal famine on the risk of proliferative diabetic retinopathy (PDR).METHODS: We studied the risk for PDR among type 2 diabetes patients exposed to perinatal famine in two independent cohorts: the Ukrainian National Diabetes Registry (UNDR) and the Hong Kong Diabetes Registry (HKDR). We analysed individuals born during the Great Famine (the Holodomor, 1932-1933) and the WWII (1941-1945) famine in 101 095 (3601 had PDR) UNDR participants. Among 3021 (251 had PDR) HKDR participants, we studied type 2 diabetes patients exposed to perinatal famine during the WWII Japanese invasion in 1942-1945.RESULTS: During the Holodomor and WWII, perinatal famine was associated with a 1.76-fold (p = 0.019) and 3.02-fold (p = 0.001) increased risk of severe PDR in the UNDR. The risk for PDR was 1.66-fold elevated among individuals born in 1942 in the HKDR (p < 0.05). The associations between perinatal famine and PDR remained statistically significant after corrections for HbA1c in available 18 507 UNDR (padditive interaction < 0.001) and in 3021 HKDR type 2 diabetes patients (p < 0.05).CONCLUSION: In conclusion, type 2 diabetes patients, exposed to perinatal famine, have increased risk of PDR compared to those without perinatal famine exposure. Further studies are needed to understand the underlying mechanisms and to extend this finding to other diabetes complications. (Less)

Aim/HypothesisFive subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian young-onset type 2 diabetes patients.MethodsWe applied the European derived centroids to the Indian type 2 diabetes patients diagnosed before 45 years of age from the WellGen (n = 1612) cohort. We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts ANDIS (n= 962) and DIREVA (n=420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).ResultsBoth Indian and European young type 2 diabetes patients were predominantly classified into severely insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severely insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to figures in Europeans (Swedish: 26% vs 68%, Finnish: 24% vs 71% respectively). A higher proportion of SIDD compared to MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterized by insulin deficiency and hyperglycemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared to MOD while the latter had higher prevalence of neuropathy.Conclusions /InterpretationOur data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European patients in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in young Indian type 2 diabetes, and suggest a need to review management strategies.

Diabetes increases the risk of bacterial infections. We investigated whether common genetic variants associate with infection susceptibility in Finnish diabetic individuals. We performed genome-wide association studies and pathway analysis for bacterial infection frequency in Finnish adult diabetic individuals (FinnDiane Study; N = 5092, Diabetes Registry Vaasa; N = 4247) using national register data on antibiotic prescription purchases. Replication analyses were performed in a Swedish diabetic population (ANDIS; N = 9602) and in a Finnish non-diabetic population (FinnGen; N = 159,166). Genome-wide data indicated moderate but significant narrow-sense heritability for infection susceptibility (h2 = 16%, P = 0.02). Variants on chromosome 2 were associated with reduced infection susceptibility (rs62192851, P = 2.23 × 10–7). Homozygotic carriers of the rs62192851 effect allele (N = 44) had a 37% lower median annual antibiotic purchase rate, compared to homozygotic carriers of the reference allele (N = 4231): 0.38 [IQR 0.22–0.90] and 0.60 [0.30–1.20] respectively, P = 0.01). Variants rs6727834 and rs10188087, in linkage disequilibrium with rs62192851, replicated in the FinnGen-cohort (P IRAK1 mediated NF-κB activation through IKK complex recruitment-pathway to be a mediator of the phenotype. Common genetic variants on chromosome 2 may associate with reduced risk of bacterial infections in Finnish individuals with diabetes.

Maf transcription factors are critical regulators of beta-cell function. We have previously shown that reduced MafA expression in human and mouse islets is associated with a pro-inflammatory gene signature. Here, we investigate if the loss of Maf transcription factors induced autoimmune processes in the pancreas. Transcriptomics analysis showed expression of pro-inflammatory as well as immune cell marker genes. However, clusters of CD4+ T and B220+ B cells were associated primarily with adult MafA−/−MafB+/−, but not MafA−/− islets. MafA expression was detected in the thymus, lymph nodes and bone marrow suggesting a novel role of MafA in regulating immune-cell function. Analysis of pancreatic lymph node cells showed activation of CD4+ T cells, but lack of CD8+ T cell activation which also coincided with an enrichment of naïve CD8+ T cells. Further analysis of T cell marker genes revealed a reduction of T cell receptor signaling gene expression in CD8, but not in CD4+ T cells, which was accompanied with a defect in early T cell receptor signaling in mutant CD8+ T cells. These results suggest that loss of MafA impairs both beta- and T cell function affecting the balance of peripheral immune responses against islet autoantigens, resulting in local inflammation in pancreatic islets.

The Precision Medicine Initiative defines precision medicine as "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." This approach will facilitate more accurate treatment and prevention strategies in contrast to a one-size-fits-all approach, in which disease treatment and prevention strategies are developed for generalized usage. Diabetes is clearly more heterogeneous than the conventional sub-classification into type 1 and type 2 diabetes. Monogenic forms of diabetes like MODY and neonatal diabetes have paved the way for precision medicine in diabetes, as carriers of unique mutations require unique treatment. Diagnosis of diabetes in the past has been dependent upon measuring one metabolite, glucose. By instead including 6 variables in a clustering analysis, we could break down diabetes into 5 distinct subgroups, with better prediction of disease progression and outcome. The severe insulin resistant diabetes (SIRD) cluster showed the highest risk of kidney disease and highest prevalence of non-alcoholic fatty liver disease whereas patients in the insulin deficient cluster 2 (SIDD) had the highest risk of retinopathy. In the future this will certainly be improved and expanded by including genetic, epigenetic and other biomarker to allow better prediction of outcome and choice of more precise treatment. This article is protected by copyright. All rights reserved. (Less)

Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein–protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system’s robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems., Robustness is a prominent feature of most biological systems, but most of the current efforts have been focused on studying homogeneous molecular networks. Here the authors propose a comprehensive framework for understanding how the interactions between genes, proteins, and metabolites contribute to the determinants of robustness.

The amplification of glucose-stimulated insulin secretion (GSIS) through incretin signaling is critical for maintaining physiological glucose levels. Incretins, like glucagon-like peptide 1 (GLP1), are a target of type 2 diabetes drugs aiming to enhance insulin secretion. Here we show that the protein phosphatase 1 inhibitor protein 1A (PPP1R1A), is expressed in β-cells and that its expression is reduced in dysfunctional β-cells lacking MafA and upon acute MafA knock down. MafA is a central regulator of GSIS and β-cell function. We observed a strong correlation of MAFA and PPP1R1A mRNA levels in human islets, moreover, PPP1R1A mRNA levels were reduced in type 2 diabetic islets and positively correlated with GLP1-mediated GSIS amplification. PPP1R1A silencing in INS1 (832/13) β-cells impaired GSIS amplification, PKA-target protein phosphorylation, mitochondrial coupling efficiency and also the expression of critical β-cell marker genes like MafA, Pdx1, NeuroD1 and Pax6. Our results demonstrate that the β-cell transcription factor MafA is required for PPP1R1A expression and that reduced β-cell PPP1R1A levels impaired β-cell function and contributed to β-cell dedifferentiation during type 2 diabetes. Loss of PPP1R1A in type 2 diabetic β-cells may explains the unresponsiveness of type 2 diabetic patients to GLP1R-based treatments.

BackgroundType 2 diabetes (T2D) is a multi-organ disease defined by hyperglycemia resulting from different disease mechanisms. Using clinical parameters measured at diagnosis (age, BMI, HbA1c, HOMA2-B, HOMA2-IR and GAD autoantibodies) adult patients with diabetes have been reproducibly clustered into five subtypes, that differed clinically with respect to disease progression and outcomes.1 In this study we use genetic information to investigate if these subtypes have distinct underlying genetic drivers.MethodsGenome-wide association (GWAS) and genetic risk score (GRS) analysis was performed in Swedish (N=12230) and Finnish (N=4631) cohorts. Family history was recorded by questionnaires.ResultsSevere insulin-deficient diabetes (SIDD) and mild obesity-related diabetes (MOD) groups had the strongest family history of T2D. A GRS including known T2D loci was strongly associated with SIDD (OR per 1 SD increment [95% CI]=1.959 [1.814-2.118]), MOD (OR 1.726 [1.607-1.855]) and mild age-related diabetes (MARD) (OR 1.771 [1.671-1.879]), whereas it was less strongly associated with severe insulin-resistant diabetes (SIRD, OR 1.244 [1.157-1.337]), which was similar to severe autoimmune diabetes (SAID, OR 1.282 [1.160-1.418]). SAID showed strong association with the GRS for T1D, whereas the non-autoimmune subtype SIDD was most strongly associated with the GRS for insulin secretion rate (P−9). SIRD showed no association with variants in TCF7L2 or any GRS reflecting insulin secretion. Instead, only SIRD was associated with GRS for fasting insulin (P=3.10×10−8). Finally, a T2D locus, rs10824307 near the ZNF503 gene was uniquely associated with MOD (ORmeta=1.266 (1.170-1.369), P=4.3×10−9).ConclusionsNew diabetes subtypes have partially different genetic backgrounds and subtype-specific risk loci can be identified. Especially the SIRD subtype stands out by having lower heritability and less involvement of beta-cell related pathways in its pathogenesis.Research in contextEvidence before this studyIn March 2018 we suggested a novel subclassification of diabetes into five subtypes. This classification was based on clustering using clinical parameters commonly measured at diabetes diagnosis (age at diabetes onset, HbA1c, bodymass index, presence of GAD autoantibodies and HOMA2 indices for insulin resistance and secretion). These subtypes differed with respect to clinical characteristics, disease progression and risk of complications, but it remained unclear to what extent these subtypes have different underlying pathologies. In our original publication we analysed a small set of genetic risk variants for diabetes and found differential associations between subtypes, suggesting potential aetiological differences.Added value of this studyIn this study we have conducted a full genome analysis of the original ANDIS cohort, including genome-wide association studies and polygenic risk score analysis with replication in an independent cohort. We have also compared heritability and prevalence of having a family history of diabetes in the subtypes.Implications of all the available evidenceWe demonstrate that stratification into subtypes facilitates identification of genetic risk loci and that the aetiology of the subtypes is at least partially distinct. These results are especially important for the future study and treatment of individuals belonging to the severe insulin-resistant diabetes (SIRD) subtype, whose pathogenesis appears to differ substantially from that of traditional T2D.

Here we show that scratch family transcriptional repressor 1 (SCRT1), a zinc finger transcriptional regulator, is a novel regulator of beta cell function. SCRT1 was found to be expressed in beta cells in rodent and human islets. In human islets, expression of SCRT1 correlated with insulin secretion capacity and the expression of the insulin (INS) gene. Furthermore, SCRT1 mRNA expression was lower in beta cells from T2D patients. siRNA-mediated Scrt1 silencing in INS-1832/13 cells, mouse- and human islets resulted in impaired glucose-stimulated insulin secretion and decreased expression of the insulin gene. This is most likely due to binding of SCRT1 to E-boxes of the Ins1 gene as shown with ChIP. Scrt1 silencing also reduced the expression of several key beta cell transcription factors. Moreover, Scrt1 mRNA expression was reduced by glucose and SCRT1 protein was found to translocate between the nucleus and the cytosol in a glucose-dependent fashion in INS-1832/13 cells as well as in a rodent model of T2D. SCRT1 was also regulated by a GSK3β-dependent SCRT1-serine phosphorylation. Taken together, SCRT1 is a novel beta cell transcription factor that regulates insulin secretion and is affected in T2D.

Gestational diabetes mellitus (GDM) is associated with poor pregnancy outcomes and increased long-term risk of metabolic diseases for both mother and child. In Tanzania, GDM prevalence increased from 0% in 1991 to 19.5% in 2016. Anaemia has been proposed to precipitate the pathogenesis of GDM. We aimed to examine the prevalence of GDM in a rural area of Tanzania with a high prevalence of anaemia and to examine a potential association between haemoglobin concentration and blood glucose during pregnancy. The participants were included in a population-based preconception, pregnancy and birth cohort study. In total, 538 women were followed during pregnancy and scheduled for an oral glucose tolerance test (OGTT) at week 32&ndash, 34 of gestation. Gestational diabetes mellitus was diagnosed according to the WHO 2013 guidelines. Out of 392 women screened, 39% (95% CI: 34.2&ndash, 44.1) had GDM, the majority of whom (94.1%) were diagnosed based solely on the fasting blood sample from the OGTT. No associations were observed between haemoglobin or ferritin and glucose measurements during pregnancy. A very high prevalence of GDM was found in rural Tanzania. In view of the laborious, costly and inconvenient OGTT, alternative methods such as fasting blood glucose should be considered when screening for GDM in low- and middle-income countries.

Background Epigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age. Results We developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set. Conclusions Our ABECs predicted adults’ chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.

Aims/hypothesis Evidence that glucose-dependent insulinotropic peptide (GIP) and/or the GIP receptor (GIPR) are involved in cardiovascular biology is emerging. We hypothesised that GIP has untoward effects on cardiovascular biology, in contrast to glucagon-like peptide 1 (GLP-1), and therefore investigated the effects of GIP and GLP-1 concentrations on cardiovascular disease (CVD) and mortality risk. Methods GIP concentrations were successfully measured during OGTTs in two independent populations (Malmö Diet Cancer–Cardiovascular Cohort [MDC-CC] and Prevalence, Prediction and Prevention of Diabetes in Botnia [PPP-Botnia]) in a total of 8044 subjects. GLP-1 (n = 3625) was measured in MDC-CC. The incidence of CVD and mortality was assessed via national/regional registers or questionnaires. Further, a two-sample Mendelian randomisation (2SMR) analysis between the GIP pathway and outcomes (coronary artery disease [CAD] and myocardial infarction) was carried out using a GIP-associated genetic variant, rs1800437, as instrumental variable. An additional reverse 2SMR was performed with CAD as exposure variable and GIP as outcome variable, with the instrumental variables constructed from 114 known genetic risk variants for CAD. Results In meta-analyses, higher fasting levels of GIP were associated with risk of higher total mortality (HR[95% CI] = 1.22 [1.11, 1.35]; p = 4.5 × 10−5) and death from CVD (HR[95% CI] 1.30 [1.11, 1.52]; p = 0.001). In accordance, 2SMR analysis revealed that increasing GIP concentrations were associated with CAD and myocardial infarction, and an additional reverse 2SMR revealed no significant effect of CAD on GIP levels, thus confirming a possible effect solely of GIP on CAD. Conclusions/interpretation In two prospective, community-based studies, elevated levels of GIP were associated with greater risk of all-cause and cardiovascular mortality within 5–9 years of follow-up, whereas GLP-1 levels were not associated with excess risk. Further studies are warranted to determine the cardiovascular effects of GIP per se.

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues., Mechanistic inference following GWAS is hampered by the lack of tissue-specific transcriptomic resources. Here the authors combine genetic variants predisposing to type 2 diabetes with human pancreatic islet RNA-seq data. They identify 7741 islet expression quantitative trait loci (eQTLs), providing a resource for functional interpretation of association signals mapping to non-coding sequence.

Expression of fat mass and obesity-associated gene (FTO) and ADP-ribosylation factor-like 15 (ARL15) in human islets is inversely correlated with HbA(1c). However, their impact on insulin secretion is still ambiguous. Here in, we investigated the role of FTO and ARL15 using GRINCH (Glucose-Responsive Insulin-secreting C-peptide-modified Human proinsulin) clonal rat beta-cells. GRINCH cells have inserted GFP into the human C-peptide insulin gene. Hence, secreted CpepGFP served to monitor insulin secretion. mRNA silencing of FTO in GRINCH cells showed a significant reduction in glucose but not depolarization-stimulated insulin secretion, whereas ARL15 silencing had no effect. A significant down-regulation of insulin mRNA was observed in FTO knockdown cells. Type-2 Diabetic islets revealed a reduced expression of FTO mRNA. In conclusion, our data suggest that fluorescent CpepGFP released from GRINCH cells may serve as a convenient marker for insulin secretion. Silencing of FTO expression, but not ARL15, inhibits insulin secretion by affecting metabolic signaling.

Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4 ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85–0.92, p = 6.3 × 10−10), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49–0.99, p = 0.041). Functional studies in Angptl4-deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D., Genetic variation in ANGPTL4 is associated with lipid traits. Here, the authors find that predicted loss-of-function variants in ANGPTL4 are associated with glucose homeostasis and reduced risk of type 2 diabetes and that Angptl4−/− mice on a high-fat diet show improved insulin sensitivity.

Dysregulation of gene expression in islets from patients with type 2 diabetes (T2D) might be causally involved in the development of hyperglycemia, or it could develop as a consequence of hyperglycemia (i.e., glucotoxicity). To separate the genes that could be causally involved in pathogenesis from those likely to be secondary to hyperglycemia, we exposed islets from human donors to normal or high glucose concentrations for 24 h and analyzed gene expression. We compared these findings with gene expression in islets from donors with normal glucose tolerance and hyperglycemia (including T2D). The genes whose expression changed in the same direction after short-term glucose exposure, as in T2D, were considered most likely to be a consequence of hyperglycemia. Genes whose expression changed in hyperglycemia but not after short-term glucose exposure, particularly those that also correlated with insulin secretion, were considered the strongest candidates for causal involvement in T2D. For example, ERO1LB, DOCK10, IGSF11, and PRR14L were downregulated in donors with hyperglycemia and correlated positively with insulin secretion, suggesting a protective role, whereas TMEM132C was upregulated in hyperglycemia and correlated negatively with insulin secretion, suggesting a potential pathogenic role. This study provides a catalog of gene expression changes in human pancreatic islets after exposure to glucose.

Aim The relative roles(s) of impaired insulin secretion vs. insulin resistance in the development of gestational diabetes mellitus depend upon multiple risk factors and diagnostic criteria. Here, we explored their relative contribution to gestational diabetes as defined by the WHO 1999 (GDM1999) and adapted WHO 2013 (GDM2013) criteria, excluding the 1-h glucose value, in a high-risk Indian population from Punjab. Methods Insulin secretion (HOMA2-B) and insulin action (HOMA2-IR) were assessed in 4665 Indian women with or without gestational diabetes defined by the GDM1999 or adapted GDM2013 criteria. Results Gestational diabetes defined using both criteria was associated with decreased insulin secretion compared with pregnant women with normal glucose tolerance. Women with gestational diabetes defined by the adapted GDM2013, but not GDM1999 criteria, were more insulin resistant than pregnant women with normal glucose tolerance, and furthermore displayed lower insulin secretion than GDM1999 women. Urban habitat, illiteracy, high age and low BMI were independently associated with reduced insulin secretion, whereas Sikh religion, increasing age and BMI, as well as a family history of diabetes were independently associated with increased insulin resistance. Conclusions Gestational diabetes risk factors influence insulin secretion and action in North Indian women in a differential manner. Gestational diabetes classified using the adapted GDM2013 compared with GDM1999 criteria is associated with more severe impairments of insulin secretion and action. This article is protected by copyright. All rights reserved.

ObjectiveTo summarize the current knowledge on epigenetic alterations in mother and offspring subjected to gestational diabetes (GDM) and indicate future topics for research.DesignSystematic review.MethodsWe performed extensive searches in PubMed, EMBASE and Google scholar, using a combination of the search terms: GDM, gestational diabetes, epigenetic(s), methylation, histone modification, histone methylation, histone acetylation, microRNA and miRNA. Studies that compared women diagnosed with GDM and healthy controls were included. Two authors independently scanned the abstracts, and all included papers were read by at least two authors. The searches were completed on October 31st, 2016.ResultsWe identified 236 articles, of which 43 were considered relevant for this systematic review. Studies published showed that epigenetic alterations could be found in both mothers with GDM and their offspring. However, differences in methodology, diagnostic criteria for GDM and populations studied, together with a limited number of published studies and small sample sizes, preclude clear conclusions about the role of epigenetic modifications in transmitting risk from GDM mothers to their offspring.ConclusionThe current research literature suggests that GDM may have impact on epigenetic modifications in the mother and offspring. However, larger studies that include multiple cohorts of GDM patients and their offspring are needed.

Publisher's version (útgefin grein)., In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding., This paper is the work of the ROHgen consortium. We thank the Sigma T2D Consortium, whose members are detailed in Supplementary Note 3. We thank the UK Biobank Resource, approved under application 19655; we acknowledge funding from the UK Medical Research Council Human Genetics Unit and MRC Doctoral Training Programme in Precision Medicine. We also thank Neil Robertson, Wellcome Trust Centre for Human Genetics, Oxford, for use of his author details management software, Authorial. Finally, we thank all the participants, researchers and funders of ROHgen cohorts. Cohort-specific acknowledgements are in Supplementary Data 2; personal acknowledgements and disclosures are in Supplementary Note 2. We thank Rachel Edwards for administrative assistance.

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

PurposeLow-income and middle-income countries such as Tanzania experience a high prevalence of non-communicable diseases (NCDs), including anaemia. Studying if and how anaemia affects growth, placenta development, epigenetic patterns and newborns’ risk of NCDs may provide approaches to prevent NCDs.ParticipantsThe FOETALforNCD (FOetal Exposure and Epidemiological Transitions: the role of Anaemia in early Life for Non-Communicable Diseases in later life) Study is a population-based preconception, pregnancy and birth cohort study (n=1415, n=538, n=427, respectively), conducted in a rural region of North-East Tanzania. All participants were recruited prior to conception or early in pregnancy and followed throughout pregnancy as well as at birth. Data collection included: maternal blood, screening for NCDs and malaria, ultrasound in each trimester, neonatal anthropometry at birth and at 1 month of age, cord blood, placental and cord biopsies for stereology and epigenetic analyses.Findings to dateAt preconception, the average age, body mass index and blood pressure of the women were 28 years, 23 kg/m2 and 117/75 mm Hg, respectively. In total, 458 (36.7%) women had anaemia (haemoglobin Hb Future plansThe project will provide new knowledge on how health, even before conception, might modify the risk of developing NCDs and how to promote better health during pregnancy. The present project ended data collection 1 month after giving birth, but follow-up is continuing through regular monitoring of growth and development and health events according to the National Road Map Strategic Plan in Tanzania. This data will link fetal adverse event to childhood development, and depending on further grant allocation, through a life course follow-up.

Objective Gestational diabetes mellitus (GDM) is a transient form of diabetes characterized by impaired insulin secretion and action during pregnancy. Population-based differences in prevalence exist which could be explained by phenotypic and genetic differences. The aim of this study was to examine these differences in pregnant women from Punjab, India and Scandinavia. Methods Eighty-five GDM/T2D loci in European and/or Indian populations from previous studies were assessed for association with GDM based on Swedish GDM criteria in 4018 Punjabi Indian and 507 Swedish pregnant women. Selected loci were replicated in Scandinavian cohorts, Radiel (N = 398, Finnish) and STORK/STORK-G (N = 780, Norwegian). Results Punjabi Indian women had higher GDM prevalence, lower insulin secretion and better insulin sensitivity than Swedish women. There were significant frequency differences of GDM/T2D risk alleles between both populations. rs7178572 at HMG20A, previously associated with GDM in South Indian and European women, was replicated in North Indian women. The T2D risk SNP rs11605924 in the CRY2 gene was associated with increased GDM risk in Scandinavian but decreased GDM risk in Punjabi Indian women. No other overlap was seen between GDM loci in both populations. Conclusions Gestational diabetes mellitus is more common in Indian than Swedish women, which partially can be attributed to differences in insulin secretion and action. There was marked heterogeneity in the GDM phenotypes between the populations which could only partially be explained by genetic differences.

Little is known about the expression and function of Retinoic acid-related orphan receptors (RORA, B, and C) in pancreatic β cells. Here in, we utilized cDNA microarray and RNA sequencing approaches to investigate the expression pattern of ROR receptors in normal and diabetic human pancreatic islets. Possible correlations between RORs expression and HbA(1c) levels as well as insulin secretory capacity in isolated human islets were evaluated. The impact of RORB and RORC expression on insulin secretion in INS-1 (832/13) cells was validated as well. While RORA was the highest expressed gene among the three RORs in human islet cells, RORC was the highest expressed in INS-1 cells (832/13) and while RORB was the lowest expressed gene in human islet cells, RORA was the highest expressed in INS-1 cells (832/13). The expression of RORB and RORC was significantly lower in diabetic/hyperglycemic donors as compared with non-diabetic counterparts. Furthermore, while the expression of RORB correlated positively with insulin secretion and negatively with HbA(1c), that of RORC correlated negatively with HbA(1c). The expression pattern of RORA did not correlate with either of the two parameters. siRNA silencing of RORB or RORC in INS-1 (832/13) cells resulted in a significant downregulation of insulin mRNA expression and insulin secretion. These findings suggest that RORB and RORC are part of the molecular cascade that regulates insulin secretion in pancreatic β cells; and insight that provides for further work on the potential therapeutic utility of RORB and RORC genes in β cell dysfunction in type 2 diabetes.

A rare loss-of-function allele p.Arg138* in SLC30A8 encoding the zinc transporter 8 (ZnT8), which is enriched in Western Finland, protects against type 2 diabetes (T2D). We recruited relatives of the identified carriers and showed that protection was associated with better insulin secretion due to enhanced glucose responsiveness and proinsulin conversion, particularly when compared with individuals matched for the genotype of a common T2D-risk allele in SLC30A8, p.Arg325. In genome-edited human induced pluripotent stem cell (iPSC)-derived β-like cells, we establish that the p.Arg138* allele results in reduced SLC30A8 expression due to haploinsufficiency. In human β cells, loss of SLC30A8 leads to increased glucose responsiveness and reduced KATP channel function similar to isolated islets from carriers of the T2D-protective allele p.Trp325. These data position ZnT8 as an appealing target for treatment aimed at maintaining insulin secretion capacity in T2D.

Understanding the molecular mechanisms behind beta cell dysfunction is essential for the development of effective and specific approaches for diabetes care and prevention. Physiological human beta cell models are needed for this work. We review the possibilities and limitations of currently available human beta cell models and how they can be dramatically enhanced using genome-editing technologies. In addition to the gold standard, primary isolated islets, other models now include immortalised human beta cell lines and pluripotent stem cell-derived islet-like cells. The scarcity of human primary islet samples limits their use, but valuable gene expression and functional data from large collections of human islets have been made available to the scientific community. The possibilities for studying beta cell physiology using immortalised human beta cell lines and stem cell-derived islets are rapidly evolving. However, the functional immaturity of these cells is still a significant limitation. CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) has enabled precise engineering of specific genetic variants, targeted transcriptional modulation and genome-wide genetic screening. These approaches can now be exploited to gain understanding of the mechanisms behind coding and non-coding diabetes-associated genetic variants, allowing more precise evaluation of their contribution to diabetes pathogenesis. Despite all the progress, genome editing in primary pancreatic islets remains difficult to achieve, an important limitation requiring further technological development. The authors’ work in this area has been supported by grants from the Academy of Finland, The Novo Nordisk Foundation and the Sigrid Jusélius Foundation. TO is also funded by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 115797 (INNODIA), which receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA), JDRF and the Leona M. and Harry B. Helmsley Charitable Trust.

Type 1 (T1D) and type 2 (T2D) diabetes are triggered by a combination of environmental and/or genetic factors. Maf transcription factors regulate pancreatic beta (&beta, )-cell function, and have also been implicated in the regulation of immunomodulatory cytokines like interferon-&beta, (IFN&beta, 1). In this study, we assessed MAFA and MAFB co-expression with pro-inflammatory cytokine signaling genes in RNA-seq data from human pancreatic islets. Interestingly, MAFA expression was strongly negatively correlated with cytokine-induced signaling (such as IFNAR1, DDX58) and T1D susceptibility genes (IFIH1), whereas correlation of these genes with MAFB was weaker. In order to evaluate if the loss of MafA altered the immune status of islets, MafA deficient mouse islets (MafA&minus, /&minus, ) were assessed for inherent anti-viral response and susceptibility to enterovirus infection. MafA deficient mouse islets had elevated basal levels of Ifn&beta, 1, Rig1 (DDX58 in humans), and Mda5 (IFIH1) which resulted in reduced virus propagation in response to coxsackievirus B3 (CVB3) infection. Moreover, an acute knockdown of MafA in &beta, cell lines also enhanced Rig1 and Mda5 protein levels. Our results suggest that precise regulation of MAFA levels is critical for islet cell-specific cytokine production, which is a critical parameter for the inflammatory status of pancreatic islets.

It is not known how ghrelin affects insulin secretion in human islets from patients with type 2 diabetes (T2D) or whether islet ghrelin expression or circulating ghrelin levels are altered in T2D. Here we sought out to identify the effect of ghrelin on insulin secretion in human islets and the impact of T2D on circulating ghrelin levels and on islet ghrelin cells. The effect of ghrelin on insulin secretion was assessed in human T2D and non-T2D islets. Ghrelin expression was assessed with RNA-sequencing (n = 191) and immunohistochemistry (n = 21). Plasma ghrelin was measured with ELISA in 40 T2D and 40 non-T2D subjects. Ghrelin exerted a glucose-dependent insulin-suppressing effect in islets from both T2D and non-T2D donors. Compared with non-T2D donors, T2D donors had reduced ghrelin mRNA expression and 75% less islet ghrelin cells, and ghrelin mRNA expression correlated negatively with HbA1c. T2D subjects had 25% lower fasting plasma ghrelin levels than matched controls. Thus, ghrelin has direct insulin-suppressing effects in human islets and T2D patients have lower fasting ghrelin levels, likely as a result of reduced number of islet ghrelin cells. These findings support inhibition of ghrelin signaling as a potential therapeutic avenue for stimulation of insulin secretion in T2D patients.

Changes in the hormone-producing pancreatic islets are central culprits in type 2 diabetes (T2D) pathogenesis. Characterization of gene expression in islets how it is altered in T2D are therefore vital in understanding islet function and T2D pathogenesis. We leveraged RNA-sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. The IGW combines expression data for a given gene with phenotypical data such as T2D status, BMI, HbA1c, insulin secretion, purity of islets, etc.), regulation of gene expression by genetic variants e.g., expression quantitative trait loci (eQTLs) and relationship with expression of islet hormones. In IGW, 285 differentially expressed genes (DEGs) were identified in T2D donors islets compared to controls. Forty percent of the DEGs showed cell-type enrichment and a large proportion of them were significantly co-expressed with islet hormone-encoding genes like glucagon (GCG, 56%), amylin (IAPP, 52%), insulin (INS, 44%) and somatostatin (SST, 24%). Inhibition of two DEGs, UNC5D and SERPINE2 impaired glucose-stimulated insulin secretion and impacted cell survival in a human beta-cell model.Significance StatementWe present Islet Gene View (IGW), a web resource facilitating information on gene expression in human pancreatic islets from organ donors easily accessible to the scientific community. In IGW, we explored RNA expression from 188 donor-islets and examined their relationship with islet phenotypes including insulin secretion and expression of genes encoding islet hormones. GWAS have shown 403 genetic variants associated with risk of type 2 diabetes (T2D) risk, however, the target genes and function of these variants in islets are largely unknown. By linking T2D risk variants to expression in islets from T2D and non-diabetic donors as well as islet phenotypes, use of IGW provided new insight into mechanisms by which variants in these loci may increase risk of T2D.

It is often unavoidable to combine data from different sequencing centers or sequencing platforms when compiling data setswith a large number of individuals. However, the different data are likely to contain specific systematic errors that will appearas SNPs. Here, we devise a method to detect systematic errors in combined data sets. To measure quality differences betweenindividual genomes, we study pairs of variants that reside on different chromosomes and co-occur in individuals. Theabundance of these pairs of variants in different genomes is then used to detect systematic errors due to batch effects.Applying our method to the 1000 Genomes data set, we find that coding regions are enriched for errors, where1% of thehigher frequency variants are predicted to be erroneous, whereas errors outside of coding regions are much rarer(<0.001%). As expected, predicted errors are found less often than other variants in a data set that was generated witha different sequencing technology, indicating that many of the candidates are indeed errors. However, predicted 1000Genomes errors are also found in other large data sets; our observation is thus not specific to the 1000 Genomes data set. Ourresults show that batch effects can be turned into a virtue by using the resulting variation in large scale data sets to detectsystematic errors.

Objective Hyperglycaemia during pregnancy increases the risk of adverse health outcomes in mother and child, but the genetic aetiology is scarcely studied. Our aims were to (1) assess the overlapping genetic aetiology between the pregnant and non-pregnant population and (2) assess the importance of genome-wide polygenic contributions to glucose traits during pregnancy, by exploring whether genetic risk scores (GRSs) for fasting glucose (FG), 2-h glucose (2hG), type 2 diabetes (T2D) and BMI in non-pregnant individuals were associated with glucose measures in pregnant women. Methods We genotyped 529 Norwegian pregnant women and constructed GRS from known genome-wide significant variants and SNPs weakly associated (p > 5 × 10−8) with FG, 2hG, BMI and T2D from external genome-wide association studies (GWAS) and examined the association between these scores and glucose measures at gestational weeks 14–16 and 30–32. We also performed GWAS of FG, 2hG and shape information from the glucose curve during an oral glucose tolerance test (OGTT). Results GRSFG explained similar variance during pregnancy as in the non-pregnant population (~5%). GRSBMI and GRST2D explained up to 1.3% of the variation in the glucose traits in pregnancy. If we included variants more weakly associated with these traits, GRS2hG and GRST2D explained up to 2.4% of the variation in the glucose traits in pregnancy, highlighting the importance of polygenic contributions. Conclusions Our results suggest overlap in the genetic aetiology of FG in pregnant and non-pregnant individuals. This was less apparent with 2hG, suggesting potential differences in postprandial glucose metabolism inside and outside of pregnancy.

Background Gestational diabetes (GDM) is a more common problem in India than in many other parts of the world but it is not known whether this is due to unique environmental factors or a unique genetic background. To address this question we examined whether the same genetic variants associated with GDM and Type 2 Diabetes (T2D) in Caucasians also were associated with GDM in North Indian women. Methods Five thousand one hundred pregnant women of gestational age 24–28 weeks from Punjab were studied by a 75 g oral glucose tolerance test (OGTT). GDM was diagnosed by both WHO1999 and 2013 criteria. 79 single nucleotide polymorphisms (SNPs) previously associated with T2D and glycemic traits (12 of them also with GDM) and 6 SNPs from previous T2D associations based on Indian population (some also with European) were genotyped on a Sequenom platform or using Taqman assays in DNA from 4018 women. Results In support of previous findings in Caucasian GDM, SNPs at KCJN11 and GRB14 loci were nominally associated with GDM1999 risk in Indian women (both p = 0.02). Notably, T2D risk alleles of the variant rs1552224 near CENTD2, rs11708067 in ADCY5 and rs11605924 in CRY2 genes associated with protection from GDM regardless of criteria applied (p

Osteopontin (OPN) is involved in various physiological processes and also implicated in multiple pathological states. It has been suggested that OPN may have a role in type 2 diabetes (T2D) by protecting pancreatic islets and interaction with incretins. However, the regulation and function of OPN in islets, especially in humans, remains largely unexplored. In this study, we performed our investigations on both diabetic mouse model SUR1-E1506K+/+ and islets from human donors. We demonstrated that OPN protein, secretion and gene expression was elevated in the diabetic SUR1-E1506K+/+ islets. We also showed that high glucose and incretins simultaneously stimulated islet OPN secretion. In islets from human cadaver donors, OPN gene expression was elevated in diabetic islets, and externally added OPN significantly increased glucose-stimulated insulin secretion (GSIS) from diabetic but not normal glycemic donors. The increase in GSIS by OPN in diabetic human islets was Ca2+ dependent, which was abolished by Ca2+-channel inhibitor isradipine. Furthermore, we also confirmed that OPN promoted cell metabolic activity when challenged by high glucose. These observations provided evidence on the protective role of OPN in pancreatic islets under diabetic condition, and may point to novel therapeutic targets for islet protection in T2D. (C) 2017 Elsevier Inc. All rights reserved.

Diabetes is a collection of diseases characterized by defective glucose homeostasis. Different diabetes types have different etiologies and their genetic architecture ranges from highly penetrant monogenetic diseases, such as MODY and neonatal diabetes, to polygenic diseases, such as type 1 and type 2 diabetes that are caused by numerous genetic variants adding up to the individual risk. While both diabetes and diabetic complications have been known to be partly heritable for a long time, identification of risk variants was originally limited to a few variants with relatively modest effect sizes. This changed with the advent of genome-wide association studies (GWAS), which has led to the identification of hundreds of common risk variants for diabetes. Still, these variants only explain part of the heritability of complex diabetes types. Further technical development in the field, such as next-generation sequencing, has recently enabled identification of rare variants. Epigenetics, epistasis, gene-environment interactions, parent-of-origin effects, and noncoding RNAs are current research areas that provide additional layers to the genetic architecture and might reveal some of the missing heritability. In this chapter, we review the genetic basis of different diabetes types and diabetic complications and the major methodological milestones that have enabled the many success stories of the last decade. (Less)

Summary Background Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis. Methods We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA 1c , and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations. Findings We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes. Interpretation We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes. Funding Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhalsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways’ relationship to T2D., Network medicine: Identifying the controlling pathways of Type 2 Diabetes Genes that structurally control regulatory networks uncover the pathways driving complex diseases. Amitabh Sharma of Brigham and Women’s Hospital and his colleagues constructed a tissue-specific gene regulatory network derived from human pancreatic islets and determined the genes that control the network, using the concept of “control centrality.” Pathways with high control centrality were significantly more associated with Type 2 Diabetes (T2D) than pathways with lower control centrality, and harbored loci that significantly affected gene expression (eQTLs) related to glucose levels. In vitro knockdown of NFATC4, jointly identified by control centrality and eQTL analysis, verified its downstream influence on genes known to be associated with T2D. Controllability in biological networks may help us pinpoint influential pathways as well as key regulators of many other complex diseases.

The secretion of insulin and glucagon from the pancreas and the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) from the gastrointestinal tract is essential for glucose homeostasis. Several novel treatment strategies for type 2 diabetes (T2D) mimic GLP-1 actions or inhibit incretin degradation (DPP4 inhibitors), but none is thus far aimed at increasing the secretion of endogenous incretins. In order to identify new potential therapeutic targets for treatment of T2D, we performed a meta-analysis of a GWAS and an exome-wide association study of circulating insulin, glucagon, GIP, and GLP-1 concentrations measured during an oral glucose tolerance test in up to 7,828 individuals. We identified 6 genome-wide significant functional loci associated with plasma incretin concentrations in or near the SLC5A1 (encoding SGLT1), GIPR, ABO, GLP2R, F13A1, and HOXD1 genes and studied the effect of these variants on mRNA expression in pancreatic islet and on metabolic phenotypes. Immunohistochemistry showed expression of GIPR, ABO, and HOXD1 in human enteroendocrine cells and expression of ABO in pancreatic islets, supporting a role in hormone secretion. This study thus provides candidate genes and insight into mechanisms by which secretion and breakdown of GIP and GLP-1 are regulated.

Finland provides unique opportunities to investigate population and medical genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assemble a comprehensive view of recent population history (≤100 generations), the timespan during which most rare disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to geographically and linguistically adjacent countries with different population histories, including 16,060 Swedes, Estonians, Russians, and Hungarians. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from over 25,000 individuals, we find that while haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland share several-fold more of their genome in identity-by-descent (IBD) segments than individuals from southwest regions containing the major cities of Helsinki and Turku. We estimate recent effective population size changes over time across regions of Finland and find significant differences between the Early and Late Settlement Regions as expected; however, our results indicate more continuous gene flow than previously indicated as Finns migrated towards the northernmost Lapland region. Lastly, we show that haplotype sharing is locally enriched among pairs of individuals sharing rare alleles by an order of magnitude, especially among pairs sharing rare disease causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

BackgroundDiabetes is presently classified into two main forms, type 1 (T1D) and type 2 diabetes (T2D), but especially T2D is highly heterogeneous. A refined classification could provide a powerful tool individualize treatment regimes and identify individuals with increased risk of complications already at diagnosis.MethodsWe applied data-driven cluster analysis (k-means and hierarchical clustering) in newly diagnosed diabetic patients (N=8,980) from the Swedish ANDIS (All New Diabetics in Scania) cohort, using five variables (GAD-antibodies, BMI, HbA1c, HOMA2-B and HOMA2-IR), and related to prospective data on development of complications and prescription of medication from patient records. Replication was performed in three independent cohorts: the Scania Diabetes Registry (SDR, N=1466), ANDIU (All New Diabetics in Uppsala, N=844) and DIREVA (Diabetes Registry Vaasa, N=3485). Cox regression and logistic regression was used to compare time to medication, time to reaching the treatment goal and risk of diabetic complications and genetic associations.FindingsWe identified 5 replicable clusters of diabetes patients, with significantly different patient characteristics and risk of diabetic complications. Particularly, individuals in the most insulin-resistant cluster 3 had significantly higher risk of diabetic kidney disease, but had been prescribed similar diabetes treatment compared to the less susceptible individuals in clusters 4 and 5. The insulin deficient cluster 2 had the highest risk of retinopathy. In support of the clustering, genetic associations to the clusters differed from those seen in traditional T2D.InterpretationWe could stratify patients into five subgroups predicting disease progression and development of diabetic complications more precisely than the current classification. This new substratificationn may help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.FundingThe funders of the study had no role in study design, data collection, analysis, interpretation or writing of the report.Research in contextEvidence before this studyThe current diabetes classification into T1D and T2D relies primarily on presence (T1D) or absence (T2D) of autoantibodies against pancreatic islet beta cell autoantigens and age at diagnosis (earlier for T1D). With this approach 75-85% of patients are classified as T2D. A third subgroup, Latent Autoimmune Diabetes in Adults (LADA,Added value of this studyHere we applied a data-driven cluster analysis of 5 simple variables measured at diagnosis in 4 independent cohorts of newly-diagnosed diabetic patients (N=14755) and identified 5 replicable clusters of diabetes patients, with significantly different patient characteristics and risk of diabetic complications. Particularly, individuals in the most insulin-resistant cluster 3 had significantly higher risk of diabetic kidney disease.Implications of the available evidenceThis new sub-stratification may help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes

Genome-wide association studies (GWAS) have identified more than 65 genetic loci associated with risk of type 2 diabetes. However, the contribution of distorted parental transmission of alleles to risk of type 2 diabetes has been mostly unexplored. Our goal was therefore to search for parent-of-origin effects (POE) among type 2 diabetes loci in families. Families from the Botnia study (n = 4,211, 1,083 families) were genotyped for 72 single-nucleotide polymorphisms (SNPs) associated with type 2 diabetes and assessed for POE on type 2 diabetes. The family-based Hungarian Transdanubian Biobank (HTB) (n = 1,463, >135 families) was used to replicate SNPs showing POE. Association of type 2 diabetes loci within families was also tested. Three loci showed nominal POE, including the previously reported variants in KCNQ1, for type 2 diabetes in families from Botnia (rs2237895: p POE = 0.037), which can be considered positive controls. The strongest POE was seen for rs7578597 SNP in the THADA gene, showing excess transmission of the maternal risk allele T to diabetic offspring (Botnia: p POE = 0.01; HTB p POE = 0.045). These data are consistent with previous evidence of allelic imbalance for expression in islets, suggesting that the THADA gene can be imprinted in a POE-specific fashion. Five CpG sites, including those flanking rs7578597, showed differential methylation between diabetic and non-diabetic donor islets. Taken together, the data emphasise the need for genetic studies to consider from which parent an offspring has inherited a susceptibility allele.

RNA sequencing of human pancreatic islets has provided important insights into the islet transcriptome but little information on the specific cells. In this issue, Segerstolpe et al. (2016) and Xin et al. (2016b) report on the transcriptome of single pancreatic cells from non-diabetic and type 2 diabetic donors. Non

Using data from a genome-wide association study of 907 individuals with childhood acute lymphoblastic leukemia (cases) and 2,398 controls and with validation in samples totaling 2,386 cases and 2,419 controls, we have shown that common variation at 9p21.3 (rs3731217, intron 1 of CDKN2A) influences acute lymphoblastic leukemia risk (odds ratio = 0.71, P = 3.01 x 10(-11)), irrespective of cell lineage.