•Diabetes is a more heterogenous disease than a simple subdivision into type 1 and type 2 diabetes assumes.•Genetics has delivered – we have learnt more about the causes of diabetes during the past 5 ...than during the past 30years, thanks to genetics.•Missing heritability of type 2 diabetes could as well be due to the fact that we do not have a clear definition of the disease.
Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart, and blood vessels. Several pathogenic processes are involved in the development of diabetes. These range from autoimmune destruction of the beta-cells of the pancreas with consequent insulin deficiency to abnormalities that result in resistance to insulin action (American Diabetes Association, 2011). The vast majority of cases of diabetes fall into two broad categories. In type 1 diabetes (T1D), the cause is an absolute deficiency of insulin secretion, whereas in type 2 diabetes (T2D), the cause is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response. However, the subdivision into two main categories represents a simplification of the real situation, and research during the recent years has shown that the disease is much more heterogeneous than a simple subdivision into two major subtypes assumes.
Worldwide prevalence figures estimate that there are 280 million diabetic patients in 2011 and more than 500 million in 2030 (http://www.diabetesatlas.org/). In Europe, about 6–8% of the population suffer from diabetes, of them about 90% has T2D and 10% T1D, thereby making T2D to the fastest increasing disease in Europe and worldwide. This epidemic has been ascribed to a collision between the genes and the environment. While our knowledge about the genes is clearly better for T1D than for T2D given the strong contribution of variation in the HLA region to the risk of T1D, the opposite is the case for T2D, where our knowledge about the environmental triggers (obesity, lack of exercise) is much better than the understanding of the underlying genetic causes. This lack of knowledge about the underlying genetic causes of diabetes is often referred to as missing heritability (Manolio et al., 2009) which exceeds 80% for T2D but less than 25% for T1D. In the following review, we will discuss potential sources of this missing heritability which also includes the possibility that our definition of diabetes and its subgroups is imprecise and thereby making the identification of genetic causes difficult.
The genetics of diabetic complications Ahlqvist, Emma; van Zuydam, Natalie R; Groop, Leif C ...
Nature reviews. Nephrology,
05/2015, Letnik:
11, Številka:
5
Journal Article
Recenzirano
The rising global prevalence of diabetes mellitus is accompanied by an increasing burden of morbidity and mortality that is attributable to the complications of chronic hyperglycaemia. These ...complications include blindness, renal failure and cardiovascular disease. Current therapeutic options for chronic hyperglycaemia reduce, but do not eradicate, the risk of these complications. Success in defining new preventative and therapeutic strategies hinges on an improved understanding of the molecular processes involved in the development of these complications. This Review explores the role of human genetics in delivering such insights, and describes progress in characterizing the sequence variants that influence individual predisposition to diabetic kidney disease, retinopathy, neuropathy and accelerated cardiovascular disease. Numerous risk variants for microvascular complications of diabetes have been reported, but very few have shown robust replication. Furthermore, only limited evidence exists of a difference in the repertoire of risk variants influencing macrovascular disease between those with and those without diabetes. Here, we outline the challenges associated with the genetic analysis of diabetic complications and highlight ongoing efforts to deliver biological insights that can drive translational benefits.
Genome-wide association studies (GWAS) have long relied on proposed statistical significance thresholds to be able to differentiate true positives from false positives. Although the genome-wide ...significance P-value threshold of 5 × 10(-8) has become a standard for common-variant GWAS, it has not been updated to cope with the lower allele frequency spectrum used in many recent array-based GWAS studies and sequencing studies. Using a whole-genome- and -exome-sequencing data set of 2875 individuals of European ancestry from the Genetics of Type 2 Diabetes (GoT2D) project and a whole-exome-sequencing data set of 13 000 individuals from five ancestries from the GoT2D and T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) projects, we describe guidelines for genome- and exome-wide association P-value thresholds needed to correct for multiple testing, explaining the impact of linkage disequilibrium thresholds for distinguishing independent variants, minor allele frequency and ancestry characteristics. We emphasize the advantage of studying recent genetic isolate populations when performing rare and low-frequency genetic association analyses, as the multiple testing burden is diminished due to higher genetic homogeneity.
The American Diabetes Association, JDRF, the European Association for the Study of Diabetes, and the American Association of Clinical Endocrinologists convened a research symposium, "The ...Differentiation of Diabetes by Pathophysiology, Natural History and Prognosis" on 10-12 October 2015. International experts in genetics, immunology, metabolism, endocrinology, and systems biology discussed genetic and environmental determinants of type 1 and type 2 diabetes risk and progression, as well as complications. The participants debated how to determine appropriate therapeutic approaches based on disease pathophysiology and stage and defined remaining research gaps hindering a personalized medical approach for diabetes to drive the field to address these gaps. The authors recommend a structure for data stratification to define the phenotypes and genotypes of subtypes of diabetes that will facilitate individualized treatment.
Summary Diabetes is a much more heterogeneous disease than the present subdivision into types 1 and 2 assumes; type 1 and type 2 diabetes probably represent extremes on a range of diabetic disorders. ...Both type 1 and type 2 diabetes seem to result from a collision between genes and environment. Although genetic predisposition establishes susceptibility, rapid changes in the environment (ie, lifestyle factors) are the most probable explanation for the increase in incidence of both forms of diabetes. Many patients have genetic predispositions to both forms of diabetes, resulting in hybrid forms of diabetes (eg, latent autoimmune diabetes in adults). Obesity is a strong modifier of diabetes risk, and can account for not only a large proportion of the epidemic of type 2 diabetes in Asia but also the ever-increasing number of adolescents with type 2 diabetes. With improved characterisation of patients with diabetes, the range of diabetic subgroups will become even more diverse in the future.
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.
Depletion of loss-of-function (LoF) mutations may provide a rank of genic functional intolerance and consequently susceptibility to disease.
Here we have studied LoF mutations in 60 706 unrelated ...individuals and show that the most intolerant quartile of ranked genes is enriched in rare and early onset diseases and explains 87% of de novo haploinsufficient OMIM mutations, 17% more than any other gene scoring tool. We detected particular enrichment in expression of the depleted LoF genes in brain (odds ratio = 1.5; P -value = 4.2e-07). By searching for de novo haploinsufficient mutations putatively associated with neurodevelopmental disorders in four recent studies, we were able to explain 81% of them. Taken together, this study provides a novel gene intolerance ranking system, called LoFtool, which may help in ranking genes of interest based on their LoF intolerance and tissue expression.
The LoFtool gene scores are available in the Supplementary data .
joaofadista@gmail.com.
Supplementary data are available at Bioinformatics online.
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.
ObjectiveThe World Health Organization (WHO) has in 2013 changed the diagnostic criteria for gestational diabetes mellitus (GDM) to acknowledge the putative effect of mildly elevated fasting plasma ...glucose (FPG) levels on pregnancy outcomes. We aimed to determine the prevalence and risk factors of GDM comparing the previous WHO 1999 criteria to the WHO 2013 criteria in North India.MethodsIn a population-based screening programme, 5100 randomly selected North Indian women were studied using a cross-sectional design with a questionnaire, venous FPG and 2-h capillary plasma glucose (PG) after a 75 g oral glucose tolerance test performed between 24 and 28 weeks of pregnancy.ResultsThe prevalence of GDM was 35% using WHO 2013 criteria vs 9% using WHO 1999 criteria. FPG measurements identified 94% of WHO 2013 GDM cases as opposed to 11% of WHO 1999 GDM cases. In contrast, 2-h PG measurements identified only 13% of WHO 2013 GDM cases compared with 96% of the WHO 1999 GDM cases. Using logistic regression with backward elimination, urban habitat, illiteracy, non-vegetarianism, increased BMI, Hindu religion and low adult height were all independent risk factors of GDM using the 1999 criteria, whereas only urban habitat, low adult height and increased age were independent risk factors of GDM using the 2013 criteria.ConclusionsIntervention studies are needed to justify the WHO 2013 GDM criteria increasing the prevalence four fold to include more than one third of North Indian pregnant women.
PDF
