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Genetic causes of type 2 diabetes

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Last Updated: 02 July 2021

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General | Latest Info

Type 2 Diabetes is a disorder characterized by abnormally high blood sugar levels. In this form of diabetes, body stops using and making insulin properly. Insulin is a hormone produced in the pancreas that helps regulate blood sugar levels. Specifically, insulin controls how much glucose is passed from blood into cells, where it is used as an energy source. When blood sugar levels are high, pancreas releases insulin to move excess glucose into cells, which reduces the amount of glucose in blood. Most people who develop type 2 Diabetes first have insulin resistance, condition in which the body's cells use insulin less efficiently than normal. As insulin resistance develop, more and more insulin is needed to keep blood sugar levels in normal range. To keep up with increasing need, insulin - producing cells in the pancreas make larger amounts of insulin. Over time, beta cells become less able to respond to blood sugar changes, leading to insulin shortages that prevent the body from reducing blood sugar levels effectively. Most people have some insulin resistance as they age, but inadequate exercise and excessive weight gain make it worse, greatly increasing the likelihood of developing type 2 Diabetes. Type 2 Diabetes can occur at any age, but it most commonly begins in middle age or later. Signs and symptoms develop slowly over years. They include frequent urination, excessive thirst, fatigue, blurred vision, tingling or loss of feeling in hands and feet, sores that do not heal well, and weight loss. If blood sugar levels are not controlled through medication or diet, type 2 Diabetes can cause long - lasting health problems including heart disease and stroke; nerve damage; and damage to kidneys, eyes, and other parts of the body. Type 2 Diabetes is the most common type of Diabetes, accounting for 90 to 95 percent of all cases. In 2015, more than 23 million people in the United States had been diagnosed with Diabetes and an additional 7 million people likely had undiagnosed Diabetes. The prevalence of Diabetes increases with age, and the disease currently affects more than 20 percent of Americans over age 65. It is the seventh leading cause of death in the United States. The risk of diabetes varies by ethnic and geographic background. In the United States, disease is most common among Native Americans and Alaska Natives. It also has a higher prevalence among people of African American or Hispanic ancestry than those of non - Hispanic white or Asian ancestry. Geographically, Diabetes is most prevalent in the Southern and Appalachian regions of the United States. The prevalence of diabetes is rapidly increasing worldwide. Due to an increase in inactive lifestyles, obesity, and other risk factors, frequency of this disease has more than quadrupled in the past 35 years. The causes of type 2 Diabetes are complex. This condition results from a combination of genetic and lifestyle factors, some of which have not been identify.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Polygenic

Analyses included 410 diabetic HNF1A mutation carriers from 203 families who were successfully genotyped for all 15 type 2 DIAbetes risk variants. We confirm strong effects of age at study, mutation position, and intrauterine hyperglycemia on severity of HNF1A DIAbetes clinical presentation. These associations were independent of polygenic risk factors. On average, patients were diagnosed 5. 1 year earlier if mother was diabetic during pregnancy, 5. 2 years earlier if mutation affected at least two HNF1A isoforms, and 0. 3 years later for every additional year of their age at study. In addition, we observed a strong effect of sex in our data, where female subjects were diagnosed 3. 0 years earlier than male subjects, but there was no association with BMI. We include those variables that had individual effect on age at diagnosis as covariates in individual and joint SNP models to reduce remaining variance in age at diagnosis and therefore increase our power to detect the effect of polygenic modifiers. We repeat these analyses excluding age at study, to make sure that its strong association with age at diagnosis does not drive SNP Association. As expect, results were not statistically significantly different to fully adjusted model. Although for some SNPs, effects on age at diagnosis were slightly stronger when age at study was exclude, SEs were larger, resulting in similar overall P values. Individual type 2 DIAbetes risk variants were not strongly associated with age at diagnosis, as shown in Table 3. However, of 15 variants, 11 risk alleles for type 2 DIAbetes in unadjusted analyses and 10 in adjusted analyses were associated with reduced age at diagnosis. When we include all 15 variants in the regression model, there was borderline evidence of an overall joint effect on age at diagnosis. 15 variants explain 6. 4% of total proportion of age at diagnosis variation, while nonpolygenic factors explain 37. 9%; combining these together, they explain 42. 1% of total Variance in HNF1A - MODY age at diagnosis in these families. We then generated a single genetic risk score representing combined genetic susceptibility for type 2 DIAbetes. In allele count model, each additional risk allele was associated with 0. 35 - year reduction in age at diagnosis. Association strength was weaker when we use unrelated probands, which most probably reflects reduced power. The correlation between decreasing age at diagnosis and increasing number of risk alleles appears to be linear for the full dataset of 410 patients. Figure 1 B presents results for 203 unrelated probands only. Looking at the impact of risk alleles on cumulative incidence of DIAbetes, effect was most noticeable around age 30 years, where DIAbetes developed in 80% of HNF1A mutation carriers with 9 - 14 polygenic risk alleles, compared with 93% with 17 - 22 risk alleles. Weight allele scores yield similar results to the allele count model.

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* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Learn the Genetics of Diabetes

Type 2 Diabetes is caused by both genetic and environmental factors. Scientists have linked several gene mutations to higher diabetes risk. Not everyone who carries mutation will get Diabetes. However, many people with Diabetes do have one or more of these mutations. It can be difficult to separate genetic risk from environmental risk. The latter is often influenced by your family members. For example, parents with healthy eating habits are likely to pass them on to the next generation. On the other hand, genetics plays a big part in determining weight. Sometimes behaviors ca take all the blame.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Tips for diabetes prevention

Interactions between genetics and the environment make it difficult to identify the definite cause of type 2 Diabetes. However, that doesnt mean you ca reduce your risk through changing your habits. The Diabetes Prevention Program Outcomes Study, large, 2012 Study of People at high risk for Diabetes, suggests that weight loss and increased physical activity can prevent or delay type 2 Diabetes. Blood glucose levels return to normal levels in some cases. Other reviews of multiple studies have reported similar results. Here are some things you can start doing today to reduce your risk For type 2 Diabetes:


Preventing Type 2 Diabetes

Many Americans are at risk for Type 2 Diabetes. Your chances of getting it depend on a combination of risk factors such as your genes and lifestyle. Risk factors include having prediabetes, which means you have blood sugar levels that are higher than normal but not high enough to be called Diabetes, being overweight or having obesity, being aged 45 or older, family history of Diabetes, being African American, Alaska Native, American Indian, Asian American, Hispanic / Latino, Native Hawaiian, or Pacific Islander Having high blood pressure Having low level of HDL cholesterol or high level of triglycerides history of Diabetes in pregnancy Having give birth to baby weighing 9 pounds or more inactive lifestyle history of heart Disease or stroke Having depression Having polycystic ovary syndrome Having acanthosis nigricans, skin condition in which Your skin become dark and thick, especially around Your neck or armpits Smoking

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

INTRODUCTION

Diabetes has been a disease of public health concern for a number of decades. It was in the 1930s when scientists made an interesting discovery that disease is actually divided into two types, as some patients were insensitive to insulin treatment then. Since then, study of disease has taken new twist with researchers looking at it from different perspectives. Although all types of diabetes result in high blood sugar levels over prolonged period, Type 1 Diabetes is said to be an autoimmune disorder which results in destruction of insulin - producing pancreatic - cells, making it insulin dependent, while Type 2 Diabetes is non - insulin dependent. Another form reported in some papers was gestational Diabetes happening in pregnant women due to hormones produced during pregnancy. Type 2 Diabetes, most common type of this complex disorder, is said to account for 85% of cases and generally happens at an older age of life. Who put Diabetes as the seventh death causing disease, affecting most communities in the world. They project that the number of people with Diabetes may increase up to 522 million by 2030. This is in line with a dramatic increase in disease prevalence from 108 million people affected in 1980 to about 422 million adults in 2014. The global prevalence of disease nearly doubled in the same period, increasing from 4. 7 to 8. 5% of the adult population. The primary cause of death in individuals with Diabetes is not the disease itself but its complications. If compared to the general non - diabetic population, patients with Type 2 Diabetes have about 7 years shorter life expectancy and this can be attributed directly to effects brought by major diabetic complications. The motility rate of people with Type 2 Diabetes tends to increase with age, with some studies suggesting that diabetic men exhibit a higher mortality risk than diabetic women. Studying for pathogenesis of Type 2 Diabetes has taken so many interesting turns over time, but as with many chronic disorders, it is understood that the disease is as result of some genetic predisposition and environmental factors. There are so many non - genetic factors that have been found and identified to be associated with Type 2 Diabetes by different researchers in different studies. These factors range from lifestyle, food and its components to some toxins and pollutants. Although environmental factors may play a role in the development of Type 2 Diabetes, even with the same environmental exposure, some individuals may be highly affected and become more susceptible to this complex disorder than others, confirming that heredity has its own impact on disease. It was until the 1980s when genetic research generally advanced and made it possible to investigate and identify some loci which could explain hereditary components. This review therefore aims at bringing literature from different researchers on genetic and non - genetic factors that are associated with Type 2 Diabetes.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

HERITABILITY OF T2D

T2d clusters in families and it is well established that the risk of developing T2D depends on both genetic and environmental factors. However, heritability estimates have varied between 25% - 80% in different studies; highest estimates are seen in those studies with longest follow - up periods. The lifetime risk of developing T2D is 40% for individuals who have one parent with T2D and almost 70% if both parents are affect. Furthermore, concordance rate of T2D in monozygotic twins is about 70%, while concordance in dizygotic twins is only 20% - 30%. Proband - wise concordance rates for monozygotic twins vary between 34% and 100%. Relative risk for first degree relatives, ie, risk of developing T2D if you have affected parent or sibling compared to the general population, is approximately 3, and ~6 if both parents are affect. However, these figures vary depending on cohort and population study. The prevalence of T2D varies widely among populations, from a few percent among Caucasians in Europe to as high as 50% among Pima Indians in Arizona. While part of observed ethnic variability could be attributed to environmental and cultural factors, some of the variation seems to depend on genetic differences. In spite of these reservations, there is no doubt the risk of T2D is partly determined by genetic factors, many of which have already been identify, and while each identified variant explains only a very small proportion of the risk of T2D in the human population, they have contributed to our understanding of disease pathogenesis. One should also keep in mind that variance explained by risk allele in population is not necessarily an indicator of its importance in specific patients, nor is it proportional to affected pathways ' importance or potential as therapeutic target.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Conclusions

A Technical revolution in the field of Genetics has allowed identification of numerous genetic variants that are associated with T2D. The genetic landscape of T2D susceptibility is as yet incomplete, thus far only explaining a small proportion of total heritability of DIAbetes. Many possibilities to dissect the architecture of T2D etiology have emerged in the form of large - scale genetic studies, Meta - Analyses and sequencing in families. It has already greatly contributed to our understanding of disease mechanisms by identifying pathways that could not be linked to DIAbetes by existing hypothetical models, even though many genetic findings are very recent and have yet to make their contribution to our knowledge about DIAbetes pathogenesis. However, one must bear in mind that DIAbetes is probably a much more diverse disease than current subdivision into T1D and T2D implies and more precise subdivision into subgroups may both facilitate investigation of T2D Genetics and pave the way for more individualized treatment. A Holistic systems biology approach will also be required to obtain a complete picture of how genetic variation lead to DIAbetes. Rapid technology development during past years holds promises that this will be possible in the not - too - distant future.


Results

- Cell K ATP channel is composed of two subunits: potassium inwardly rectifying channel, subfamily J, member 11, and sulfonylurea receptor 1. Flux of K + ions, and therefore electrical activity of cell membranes, is regulated by K ATP channels. Release of insulin by - cells is initiated by this electrical activity, and thus, is dependent on the function of K ATP channels. 15 Several mutations within the ABCC8 gene have been associated with hyperinsulinemia in infancy. 15 More common variations within this gene have also been studied in relation to T2D. Among them, 3T allele of exon 16 3C > T splice acceptor site variant has been associated with - cell dysfunction as well as with increased risk of T2D. 16 - 19 Another variant, Arg1273Arg, has been associated with T2D - related phenotypes such as hyperinsulinemia in nondiabetic Mexican Americans. 20 only one study, to our knowledge, examined the relationship between GDM and variants within the ABCC8 gene. Rissanen et al. 19 examined eight variants identified by molecular screening of gene in 42 Finnish patients with GDM. Among variants examine, exon 16 3T allele and 1273G allele of Arg1273Arg variant were more frequent in women with GDM compared with 377 control subjects. No significant difference in allele frequency was observed for other variants identify. Despite these associations with GDM, these variants were not associated with variations in insulin secretion as demonstrated previously. 20 Because of the inclusion of men as control subjects and small number of subjects, this positive association needs support from further studies. Kcnj11 encodes other subunit of - cell ATP - sensitive potassium channel. A Variant has been identified in the KCNJ11 gene that results in substitution of lysine for glutamic acid, Glu23Lys. In vitro studies have demonstrated that variant induces overactivity of pancreatic - cell K ATP channels, leading to decreased insulin secretion. 21 give role of KCNJ11 in insulin secretion, number of studies have investigated KCNJ11 Glu23Lys variant in relation to T2D. Data from meta - analysis suggest that the population - attributable risk for T2D was 6. 2% for KCNJ11 Lys23 / Lys23 genotype and 10. 1% for KCNJ11 Glu23 / Lys23 and Lys23 / Lys23 genotypes combine. 22 Another meta - analysis combining association studies among whites show that Glu23Lys variant was significantly associated with T2D with Lys23 / Lys23 homozygosity being significantly more frequent in patients with T2D than in control subjects. 23 despite strong evidence of an association between this variant within KCNJ11 gene and T2D, only one published study has reported the effect of this polymorphism on GDM. Shaat et al. 24 found an increased frequency of Lys23 allele among 588 Scandinavian women with GDM compared with 1189 nondiabetic pregnant controls. These results are consistent with impaired insulin secretion associated with KCNJ11 Glu23Lys variant. However, association did not adjust for potential confounders such as age and BMI. Uncoupling proteins are members of the larger family of mitochondrial anion carrier proteins.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

(1) The candidate gene approach

A great number of studies in various populations have suggested an association between several single - nucleotide polymorphisms and Type 2 Diabetes. For example, transcription factor 7 - like 2 is a highly reliable predisposing Gene for Type 2 Diabetes. In addition, recent Genome - Wide Association Studies have provided new susceptible loci for Type 2 Diabetes. Gwas in French subjects, for example, identified rs13266634, nonsynonymous SNP on solute carrier family 30 member 8 Gene, as polymorphism involved in Type 2 Diabetes susceptibility. The study also reported an association between Type 2 Diabetes and rs1111875, as well as rs7923837, located in hematopoietically expressed homeobox Gene. These associations were replicated in three independent GWASs in various populations. Additional susceptible SNPs were independently identified in insulin - like growth factor 2 mRNA - binding protein 2 Gene. Involvement of SNPs rs10811661, located upstream of cyclin - Dependent kinase inhibitor genes CDKN2A and CDKN2B, and rs7754840 / rs7756992, located in CDK5 regulatory subunit - associate protein 1 - like 1 Gene, has also been suggest. A recent population - base study in Danish subjects replicated susceptible Association of HHEX rs111875, CDKN2A / B rs10811661, and IGF2BP2 rs4402960 with Type 2 Diabetes. Findings from previous GWASs, however, cannot be extrapolated to other populations with different lifestyles and environmental backgrounds. In particular, genetic background for Type 2 Diabetes development in East Asians, who show lower basal insulin secretion and mark decrease in insulin release in response to development of glucose tolerance, appears to be different from that in Caucasians or individuals of European origin. Further, SNP frequency differences are suggested to be an additional factor influencing Type 2 Diabetes susceptibility. Here, based on recent GWAS, we conducted a replication study of candidate SNPs associated with Type 2 Diabetes in Japanese diabetic subjects, as well as in the general Japanese population sample. Meta - analysis of Type 2 Diabetes genetic Association Studies in Japanese. Estimation of odds ratios and 95% CIs in each study are displayed as closed square and horizontal line, respectively. Square size represents study weighting. The combined odds ratio represents diamond. Study A, present Study; Study B, Hayashi et al.; Study C, Horikoshi et al.; Study D, Horikawa et al.; Study E, Furukawa et al.; Study F, Horikoshi et al.; Study G, Omori et al.


Introduction

Type 2 DIAbetes is a serious metabolic disease associated with increased risk of premature death and substantial disability, largely mediated through its adverse effects on vasculature. The prevalence of disease is increasing, and World Health Organisation estimates suggest that by 2025 there will be 300 million affected individuals worldwide. The disorder is characterise by a combination of impaired insulin secretion and insulin action, both of which precede and predict onset of disease. Through its adverse impact on insulin action, obesity is a major risk factor for disease. Although environmental factors, both post - and prenatal, play an important role in determining risk of disease, substantial body of evidence supports the notion that disease susceptibility is influenced by inherited factors. While the molecular basis for several uncommon Mendelian forms of Type 2 DIAbetes has been define, nature and range of allelic variants conferring susceptibility to more common forms of this disorder remain poorly define. Many investigators have embarked on attempts to identify DIAbetes susceptibility genes through genome - wide linkage - base approaches using multigenerational pedigrees and / or large numbers of affected sibpairs. Regions of significant linkage, some of which have been replicated in more than one study, have been identify. To date, however, only Calpain 10 has emerged from such studies as new putative diabetogene. While some subsequent studies have supported role for CAPN10 alleles originally described as susceptibility alleles, others have found associations with different alleles and some have found no association with this gene. Positional cloning effort has been supplemented by a large number of studies examining specific candidate genes using a variety of methodologies, mostly of case - control association design. Although many positive reports have emerge, few have been consistently replicate. Of these candidates, most compelling evidence to date, generated from Meta - analysis of multiple published studies, is that the common amino acid variant in the N - terminus of nuclear receptor peroxisome proliferator - activate receptor confers significant protection against development of Type 2 DIAbetes. More recently, evidence has accumulated supporting role for E23K variant of KCNJ11 in Type 2 DIAbetes predisposition. Whole - genome association studies in large case - control populations may ultimately have the greatest power to detect alleles of small but significant effects on susceptibility to common diseases such as Type 2 DIAbetes. As yet, however, resource implications of such an approach are prohibitive. In the meantime, knowledge of both mammalian biology and disease pathogenesis is progressing rapidly, and it is possible to identify large panel of known genes, dysfunction of which might reasonably be considered likely to contribute to Type 2 DIAbetes. In this study, we have identified 152 informative single nucleotide polymorphisms in 71 such genes and, using these, have examined their association with Type 2 DIAbetes and related intermediate phenotypes in Caucasian subjects from the United Kingdom.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

(2) Genome wide scan

One OF the major drawbacks of candidate gene approach is that it will not lead to identification of entirely new GENES or pathways involved in Type 2 Diabetes Mellitus. In order to identify new GENES for Type 2 Diabetes Mellitus, Genome wide scans using polymorphic markers need to be perform. However, classical approach OF gene localisation by linkage analysis in multigenerational families is not the most suitable strategy for Type 2 Diabetes Mellitus for several reasons. Firstly, there is lack OF Mendelian inheritance pattern; secondly, mean age OF diagnosis is around 60 years. As a consequence, one or both OF patient's parents are often no longer available for study. Thirdly, only affected subjects can be used for linkage studies because of reduced and age dependent penetrance. Hence, it is hard to obtain families with enough Type 2 Diabetes Mellitus patients. In addition, genetic HEterogeneity can become a problem as mutations in any one OF several GENES may result in identical phenotypes, or chromosomal regions may cosegregate with disease in some families but not in others. A Non - parametric analysis method can overcome these problems, since this would require no knowledge OF mode OF inheritance OF disease, disease allele frequencies, or penetrance. 38 commonly used non - parametric genetic mapping approach is affect sib pair approach using randomly spaced polymorphic markers. The ASP approach is discussed in detail in box 1. Using ASPs in Genome wide scans generally requires large numbers OF ASPs to obtain sufficient power for detecting linkage to give value OF S. 39 40 This strategy is also very expensive and it used to be extremely time - consuming. However, technological improvements, such as capillary sequencing equipment and faster computers, have decreased the time required enormously. The most efficient and cost - beneficial way of performing Genome wide scan using ASP is stag searching. The initial genome scan is carried out with a sparse marker set. Regions OF interest should exceed the threshold LOD Score OF 1. 0 It has been shown that power exceeds 90% in the sample size OF 200 ASPs once R is greater than 1. 7, give LOD OF 1. 0 39 42 Loci with delicate effects are not missed when a lower threshold is used. However, this strategy also increases the false positive rate. Subsequently, regions of interest are investigated with a denser marker set. The threshold for significant linkage would be LOD Score OF 3. 3 42 43 49 three stage strategy, with increasing thresholds AT each stage, is the most powerful approach to adopt in Genome scan. 42 50 alternative stag strategy, know as sample splitting, is to perform initial screening on part OF sample and to follow up on interesting Loci in the whole sample. 43 44 efficient study design is an important aspect of any Genome wide scan. Different types of cohorts, consisting of nuclear families, multigenerational families, or affected sib pairs, can be used

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

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