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, HbA
, 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.
Type 2 diabetes (T2D) is a complex disease that is caused by a complex interplay between genetic, epigenetic and environmental factors. While the major environmental factors, diet and activity level, ...are well known, identification of the genetic factors has been a challenge. However, recent years have seen an explosion of genetic variants in risk and protection of T2D due to the technical development that has allowed genome-wide association studies and next-generation sequencing. Today, more than 120 variants have been convincingly replicated for association with T2D and many more with diabetes-related traits. Still, these variants only explain a small proportion of the total heritability of T2D. In this review, we address the possibilities to elucidate the genetic landscape of T2D as well as discuss pitfalls with current strategies to identify the elusive unknown heritability including the possibility that our definition of diabetes and its subgroups is imprecise and thereby makes the identification of genetic causes difficult.
Type 2 diabetes (T2D) is one of the fastest increasing diseases worldwide. Although it is defined by a single metabolite, glucose, it is increasingly recognized as a highly heterogeneous disease with ...varying clinical manifestations. Identification of different subtypes at an early stage of disease when complications might still be prevented could hopefully allow for more personalized medicine. An important step toward precision medicine would be to target the right resources to the right patients, thereby improving patient health and reducing health costs for the society. More well-defined disease populations also offer increased power in experimental, genetic and clinical studies. In a recent study, we used six clinical variables (glutamate decarboxylase autoantibodies, age at onset of diabetes, glycated hemoglobin, BMI and simple measures of insulin resistance and insulin secretion (so called HOMA estimates) to cluster adult-onset diabetes patients into five subgroups. These subgroups have been robustly reproduced in several populations worldwide and are associated with different risks of diabetic complications and responses to treatment. Importantly, the group with severe insulin-deficient diabetes had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes group has the highest risk for diabetic kidney disease (DKD) and fatty liver. This emphasizes the key role of insulin resistance in the pathogenesis of DKD and fatty liver in T2D. In conclusion, this novel subclassification, breaking down T2D in clinically meaningful subgroups, provides the prerequisite framework for expanded personalized medicine in diabetes beyond what is already available for monogenic and to some extent type 1 diabetes.
Significance We provide a comprehensive catalog of novel genetic variants influencing gene expression and metabolic phenotypes in human pancreatic islets. The data also show that the path from ...genetic variation (SNP) to gene expression is more complex than hitherto often assumed, and that we need to consider that genetic variation can also influence function of a gene by influencing exon usage or splice isoforms (sQTL), allelic imbalance, RNA editing, and expression of noncoding RNAs, which in turn can influence expression of target genes.
Genetic variation can modulate gene expression, and thereby phenotypic variation and susceptibility to complex diseases such as type 2 diabetes (T2D). Here we harnessed the potential of DNA and RNA sequencing in human pancreatic islets from 89 deceased donors to identify genes of potential importance in the pathogenesis of T2D. We present a catalog of genetic variants regulating gene expression (eQTL) and exon use (sQTL), including many long noncoding RNAs, which are enriched in known T2D-associated loci. Of 35 eQTL genes, whose expression differed between normoglycemic and hyperglycemic individuals, siRNA of tetraspanin 33 (TSPAN33), 5′-nucleotidase, ecto (NT5E), transmembrane emp24 protein transport domain containing 6 (TMED6), and p21 protein activated kinase 7 (PAK7) in INS1 cells resulted in reduced glucose-stimulated insulin secretion. In addition, we provide a genome-wide catalog of allelic expression imbalance, which is also enriched in known T2D-associated loci. Notably, allelic imbalance in paternally expressed gene 3 (PEG3) was associated with its promoter methylation and T2D status. Finally, RNA editing events were less common in islets than previously suggested in other tissues. Taken together, this study provides new insights into the complexity of gene regulation in human pancreatic islets and better understanding of how genetic variation can influence glucose metabolism.
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.
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.
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
, 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.
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.
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.
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, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.
Aquaglyceroporin 7 (AQP7) facilitates glycerol flux across the plasma membrane with a critical physiological role linked to metabolism, obesity, and associated diseases. Here, we present the ...single-particle cryo-EM structure of AQP7 determined at 2.55 Å resolution adopting two adhering tetramers, stabilized by extracellularly exposed loops, in a configuration like that of the well-characterized interaction of AQP0 tetramers. The central pore, in-between the four monomers, displays well-defined densities restricted by two leucine filters. Gas chromatography mass spectrometry (GC/MS) results show that the AQP7 sample contains glycerol 3-phosphate (Gro3P), which is compatible with the identified features in the central pore. AQP7 is shown to be highly expressed in human pancreatic α- and β- cells suggesting that the identified AQP7 octamer assembly, in addition to its function as glycerol channel, may serve as junction proteins within the endocrine pancreas.
Type 2 diabetes (T2D) is one of the fastest increasing diseases worldwide. Although it is defined by a single metabolite, glucose, it is increasingly recognized as a highly heterogeneous disease with ...varying clinical manifestations. Identification of different subtypes at an early stage of disease when complications might still be prevented could hopefully allow for more personalized medicine. An important step towards precision medicine would be to target the right resources to the right patients, thereby improving patient health and reducing health costs for the society. More well-defined disease populations also offer increased power in experimental, genetic and clinical studies. In a recent study, we used six clinical variables (GAD autoantibodies, age at onset of diabetes, HbA1c, BMI, and simple measures of insulin resistance and insulin secretion (so called HOMA estimates) to cluster adult-onset diabetes patients into five subgroups. These subgroups have been robustly reproduced in several populations worldwide and are associated with different risks of diabetic complications and responses 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 has the highest risk for diabetic kidney disease (DKD) and fatty liver. This emphasizes the key role of insulin resistance in the pathogenesis of DKD and fatty liver in T2D. In conclusion, this novel sub-classification, breaking down T2D in clinically meaningful subgroups, provides the prerequisite framework for expanded personalized medicine in diabetes beyond what is already available for monogenic and to some extent type 1 diabetes.
Genetic variants involved in vitamin D metabolism have been associated with diabetes and related syndromes/diseases. We wanted to investigate possible associations of polymorphisms in genes involved ...in vitamin D metabolism with indices of insulin resistance and insulin secretion, and also with development of diabetes after gestational diabetes mellitus (GDM).
We have studied 376 women with previous GDM. Eight single nucleotide polymorphisms (SNPs) in the genes for vitamin D receptor (VDR) rs731236, rs7975232, rs10735810, and rs1544410, vitamin D binding protein (DBP) rs7041 and rs4588, and cytochrome P450 family 27 subfamily B member 1 (CYP27B1) rs10877012 and rs4646536 were genotyped by TaqMan Allelic Discrimination Assay using the Quantstudio 7 Flex system. A 75-g oral glucose tolerance test (OGTT) was performed 1-2 years postpartum. The homeostasis model assessment of insulin resistance (HOMA-IR) and the disposition index (insulinogenic index: I30/G30)/HOMA-IR were used to calculate insulin resistance and insulin secretion, respectively. Serum samples for determination of 25(OH)D3 were collected at the time of the OGTT. Manifestation of diabetes was followed up to five years postpartum.
After adjustment for BMI, age, and ethnicity, the A-allele of the VDR rs1544410 polymorphism was found to be associated with increased disposition index (difference per allele = 3.56, 95% CI: 0.4567-6.674; p = 0.03). The A-allele of the DBP rs7041 polymorphism was found to be associated with 25(OH)D3 levels (difference in nmol/L per allele = -5.478, 95% CI: -8.315 to -2.641; p = 0.0002), as was the T-allele of the DBP rs4588 polymorphism (OR = -6.319, 95% CI: -9.466 to -3.171; p = 0.0001). None of the SNPs were significantly associated with HOMA-IR or postpartum diabetes.
This study provides evidence that the rs1544410 polymorphism of the VDR gene may be associated with increased insulin secretion in women after pregnancy complicated by GDM. Further studies in other populations are needed to confirm the results.
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.
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