Insights into the Genetic Susceptibility to Type 2 Diabetes from Genome-Wide Association Studies of Obesity-Related Traits

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Relationship between GCKR gene rs780094 polymorphism and type 2 diabetes with albuminuria

BACKGROUND

Diabetic kidney disease is one of the common complications of type 2 diabetes (T2D). There are no typical symptoms in the early stage, and the disease will progress to moderate and late stage when albuminuria reaches a high level. Treatment is difficult and the prognosis is poor. At present, the pathogenesis of diabetic kidney disease is still unclear, and it is believed that it is associated with genetic and environmental factors.

AIM

To explore the relationship between the glucokinase regulatory protein (GCKR) gene rs780094 polymorphism and T2D with albuminuria.

METHODS

We selected 252 patients (126 males and 126 females) with T2D admitted to our hospital from January 2020 to October 2020, and 66 healthy people (44 females and 22 males). According to the urinary albumin/creatinine ratio, the subjects were divided into group I (control), group II (T2D with normoalbuminuria), group III (T2D with microalbuminuria), and group IV (T2D with macroalbuminuria). Additionly, the subjects were divided into group M (normal group) or group N (albuminuria group) according to whether they developed albuminuria. We detected the GCKR gene rs780094 polymorphism (C/T) of all subjects, and measured the correlation between GCKR gene rs780094 polymorphism (C/T) and T2D with albuminuria.

RESULTS

Gene distribution and genotype distribution among groups I-IV accorded with the Hardy-Weinberg equilibrium. Genotype frequency was significantly different among the four groups (P = 0.048, χ2 = 7.906). T allele frequency in groups II, III, and IV was significantly higher than that in group I. Logistic regression analysis of the risk factors for T2D with albuminuria showed that the CT + TT genotype (odds ratio = 1.710, 95% confidence interval: 1.172-2.493) was a risk factor.

CONCLUSION

CT + TT genotype is a risk factor for T2D with albuminuria. In the future, we can assess the risk of individuals carrying susceptible genes to delay the onset of T2D.

Influence of polymorphisms in IRS1, IRS2, MC3R, and MC4R on metabolic and inflammatory status and food intake in Brazilian adults: An exploratory pilot study

Polymorphisms in genes of leptin-melanocortin and insulin pathways have been associated with obesity and type 2 diabetes. We hypothesized that polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory markers and food intake composition in Brazilian subjects. This exploratory pilot study included 358 adult subjects. Clinical, anthropometric, and laboratory data were obtained through interview and access to medical records. The variants IRS1 rs2943634 A˃C, IRS2 rs1865434 C>T, MC3R rs3746619 C>A, and MC4R rs17782313 T>C were analyzed by real-time polymerase chain reaction. Food intake composition was assessed in a group of subjects with obesity (n = 84) before and after a short-term nutritional counseling program (9 weeks). MC4R rs17782313 was associated with increased risk of obesity (P = .034). Multivariate linear regression analysis adjusted by covariates indicated associations of IRS2 rs1865434 with reduced low-density lipoprotein cholesterol and resistin, MC3R rs3746619 with high glycated hemoglobin, and IRS1 rs2943634 and MC4R rs17782313 with increased high-sensitivity C-reactive protein (P < .05). Energy intake and carbohydrate and total fat intakes were reduced after the diet-oriented program (P < .05). Multivariate linear regression analysis showed associations of IRS2 rs1865434 with high basal fiber intake, IRS1 rs2943634 with low postprogram carbohydrate intake, and MC4R rs17782313 with low postprogram total fat and saturated fatty acid intakes (P < .05). Although significant associations did not survive correction for multiple comparisons using the Benjamini-Hochberg method in this exploratory study, polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory status in Brazilian adults. IRS1 and MC4R variants may influence carbohydrate, total fat, and saturated fatty acid intakes in response to a diet-oriented program in subjects with obesity.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

Effects of Crocodile Oil (Crocodylus siamensis) on Liver Enzymes: Cytochrome P450 and Glutathione S-Transferase Activities in High-fat DietFed Rats

Crocodile oil is a highly effective treatment for ailments ranging from skin conditions to cancer. However, the effects of the oil on liver detoxification pathways are not well studied. This study aimed to investigate the effects of crocodile oil on the detoxification enzyme activities and the mRNA expressions of cytochrome P450 1A2 (CYP1A2), cytochrome P450 2E1 (CYP2E1), and glutathione S-transferase (GST) in rats. The rats were divided into four groups (n = 7/group): rats received a standard diet (C), a high-fat diet or HFD (H), and HFD with 1 ml (HCO1) and 3 ml (HCO3) of the oil per kg body weight. Interestingly, the oil yields from this study presented alpha-linolenic acid (0.96%) at similar levels compared with fish oil. The results revealed that HFD significantly increased the activity and relative gene expression of CYP1A2 in the H group (P < 0.05), whereas 3% crocodile oil normalized the enzyme activities compared to the C group. This suggested inhibiting the HFD-induced expression of CYP1A2 mediated by the omega-3 fatty acids found in the oil. Also, crocodile oil supplementation did not reduce the activities of GST. However, the relative gene expression of GSTA1 was significantly decreased (P < 0.05) in the HCO1 and HCO3 groups compared to the H group, which might be attributed to the lower lipid peroxidation that occurred in the liver tissues. Therefore, it could be suggested that using crocodile oil could help in liver detoxification through the CYP1A2 even when offered with a HFD.

Unraveling Gut Microbiota Signatures Associated with PPARD and PARGC1A Genetic Polymorphisms in a Healthy Population

Recent studies have revealed the importance of the gut microbiota in the regulation of metabolic phenotypes of highly prevalent metabolic diseases such as obesity, type II diabetes mellitus (T2DM) and cardiovascular disease. Peroxisome proliferator-activated receptors (PPARs) are a family of ligand-activated nuclear receptors that interact with PPAR-γ co-activator-1α (PPARGC1A) to regulate lipid and glucose metabolism. Genetic polymorphisms in PPARD (rs 2267668; A/G) and PPARGC1A (rs 8192678; G/A) are linked to T2DM. We studied the association between the single-nucleotide polymorphisms (SNPs) rs 2267668 and rs 8192678 and microbiota signatures and their relation to predicted metagenome functions, with the aim of determining possible microbial markers in a healthy population. Body composition, physical exercise and diet were characterized as potential confounders. Microbiota analysis of subjects with PPARGC1A (rs 8192678) and PPARD (rs 2267668) SNPs revealed certain taxa associated with the development of insulin resistance and T2DM. Kyoto encyclopedia of gene and genomes analysis of metabolic pathways predicted from metagenomes highlighted an overrepresentation of ABC sugar transporters for the PPARGC1A (rs 8192678) SNP. Our findings suggest an association between sugar metabolism and the PPARGC1A rs 8192678 (G/A) genotype and support the notion of specific microbiota signatures as factors related to the onset of T2DM.

Dyslipidemia, insulin resistance, and impairment of placental metabolism in the offspring of obese mothers

Obesity is a chronic condition associated with dyslipidemia and insulin resistance. Here, we show that the offspring of obese mothers are dyslipidemic and insulin resistant from the outset.Maternal and cord blood and placental tissues were collected following C-section at term. Patients were grouped as being normal weight (NW, BMI = 18-24.9) or obese (OB, BMI ≥ 30), and separated by fetal sex. We measured plasma lipids, insulin, and glucose in maternal and cord blood. Insulin resistance was quantified using the HOMA-IR. Placental markers of lipid and energy metabolism and relevant metabolites were measured by western blot and metabolomics, respectively.For OB women, total cholesterol was decreased in both maternal and cord blood, while HDL was decreased only in cord blood, independent of sex. In babies born to OB women, cord blood insulin and insulin resistance were increased. Placental protein expression of the energy and lipid metabolism regulators PGC1α, and SIRT3, ERRα, CPT1α, and CPT2 decreased with maternal obesity in a sex-dependent manner (P < 0.05). Metabolomics showed lower levels of acylcarnitines C16:0, C18:2, and C20:4 in OB women's placentas, suggesting a decrease in β-oxidation. Glutamine, glutamate, alpha-ketoglutarate (αKG), and 2-hydroxyglutarate (2-HG) were increased, and the glutamine-to-glutamate ratio decreased (P < 0.05), in OB placentas, suggesting induction of glutamate into αKG conversion to maintain a normal metabolic flux.Newly-born offspring of obese mothers begin their lives dyslipidemic and insulin resistant. If not inherited genetically, such major metabolic perturbations might be explained by abnormal placental metabolism with potential long-term adverse consequences for the offspring's health and wellbeing.

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.

The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach

BACKGROUND AND LIMITATIONS

Impaired wound healing (WH) and chronic inflammation are hallmarks of non-communicable diseases (NCDs). However, despite WH being a recognized player in NCDs, mainstream therapies focus on (un)targeted damping of the inflammatory response, leaving WH largely unaddressed, owing to three main factors. The first is the complexity of the pathway that links inflammation and wound healing; the second is the dual nature, local and systemic, of WH; and the third is the limited acknowledgement of genetic and contingent causes that disrupt physiologic progression of WH.

PROPOSED APPROACH

Here, in the frame of Predictive, Preventive, and Personalized Medicine (PPPM), we integrate and revisit current literature to offer a novel systemic view on the cues that can impact on the fate (acute or chronic inflammation) of WH, beyond the compartmentalization of medical disciplines and with the support of advanced computational biology.

CONCLUSIONS

This shall open to a broader understanding of the causes for WH going awry, offering new operational criteria for patients' stratification (prediction and personalization). While this may also offer improved options for targeted prevention, we will envisage new therapeutic strategies to reboot and/or boost WH, to enable its progression across its physiological phases, the first of which is a transient acute inflammatory response versus the chronic low-grade inflammation characteristic of NCDs.

The Importance of Precision Medicine in Type 2 Diabetes Mellitus (T2DM): From Pharmacogenetic and Pharmacoepigenetic Aspects

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) is a worldwide disorder as the most important challenges of health-care systems. Controlling the normal glycaemia greatly profit long-term prognosis and gives explanation for early, effective, constant, and safe intervention.

MATERIAL AND METHODS

Finding the main genetic and epigenetic profile of T2DM and the exact molecular targets of T2DM medications can shed light on its personalized management. The comprehensive information of T2DM was earned through the genome-wide association study (GWAS) studies. In the current review, we represent the most important candidate genes of T2DM like CAPN10, TCF7L2, PPAR-γ, IRSs, KCNJ11, WFS1, and HNF homeoboxes. Different genetic variations of a candidate gene can predict the efficacy of T2DM personalized strategy medication.

RESULTS

SLCs and AMPK variations are considered for metformin, CYP2C9, KATP channel, CDKAL1, CDKN2A/2B and KCNQ1 for sulphonylureas, OATP1B, and KCNQ1 for repaglinide and the last but not the least ADIPOQ, PPAR-γ, SLC, CYP2C8, and SLCO1B1 for thiazolidinediones response prediction.

CONCLUSION

Taken everything into consideration, there is an extreme need to determine the genetic status of T2DM patients in some known genetic region before planning the medication strategies.

A Comprehensive Genome-Wide and Phenome-Wide Examination of BMI and Obesity in a Northern Nevadan Cohort

The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.

Interplay of an Obesity-Based Genetic Risk Score with Dietary and Endocrine Factors on Insulin Resistance

This study aimed to nutrigenetically screen gene-diet and gene-metabolic interactions influencing insulin resistance (IR) phenotypes. A total of 232 obese or overweight adults were categorized by IR status: non-IR (HOMA-IR (homeostatic model assessment - insulin resistance) index ≤ 2.5) and IR (HOMA-IR index > 2.5). A weighted genetic risk score (wGRS) was constructed using 95 single nucleotide polymorphisms related to energy homeostasis, which were genotyped by a next generation sequencing system. Body composition, the metabolic profile and lifestyle variables were evaluated, where individuals with IR showed worse metabolic outcomes. Overall, 16 obesity-predisposing genetic variants were associated with IR (p < 0.10 in the multivariate model). The wGRS strongly associated with the HOMA-IR index (adj. R squared = 0.2705, p < 0.0001). Moreover, the wGRS positively interacted with dietary intake of cholesterol (P int. = 0.002), and with serum concentrations of C-reactive protein (P int. = 0.008) regarding IR status, whereas a negative interaction was found regarding adiponectin blood levels (P int. = 0.006). In conclusion, this study suggests that interactions between an adiposity-based wGRS with nutritional and metabolic/endocrine features influence IR phenotypes, which could facilitate the prescription of personalized nutrition recommendations for precision prevention and management of IR and diabetes.

Obesity and cardiovascular risk: a call for action from the European Society of Hypertension Working Group of Obesity, Diabetes and the High-risk Patient and European Association for the Study of Obesity: part A: mechanisms of obesity induced hypertension, diabetes and dyslipidemia and practice guidelines for treatment

: Obesity is a key factor for cardiovascular diseases and complications. Obesity is associated with hypertension, dyslipidemia and type II diabetes, which are the major predictors of cardiovascular disease in the future. It predisposes for atrial fibrillation, heart failure, sudden cardiac death, renal disease and ischemic stroke that are the main causes of cardiovascular hospitalization and mortality. As obesity and the cardiovascular effects on the vessels and the heart start early in life, even from childhood, it is important for health policies to prevent obesity very early before the disease manifestation emerge. Key roles in the prevention are strategies to increase physical exercise, reduce body weight and to prevent or treat hypertension, lipids disorders and diabetes earlier and efficiently to prevent cardiovascular complications.Epidemiology and mechanisms of obesity-induced hypertension, diabetes and dyslipidemia will be reviewed and the role of lifestyle modification and treatment strategies in obesity will be updated and analyzed. The best treatment options for people with obesity, hypertension, diabetes and dyslipidemia will discussed.

High-Diversity Mouse Populations for Complex Traits

Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species.

Nutrigenetics, epigenetics and gestational diabetes: consequences in mother and child

Gestational Diabetes Mellitus (GDM) is the most common metabolic condition during pregnancy and may result in short- and long-term complications for both mother and offspring. The complexity of phenotypic outcomes seems influenced by genetic susceptibility, nutrient-gene interactions and lifestyle interacting with clinical factors. There is strong evidence that not only the adverse genetic background but also the epigenetic modifications in response to nutritional and environmental factors could influence the maternal hyperglycemia in pregnancy and the foetal metabolic programming. In this view, the correlation between epigenetic modifications and their transgenerational effects represents a very interesting field of study. The present review gives insight into the role of gene variants and their interactions with nutrients in GDM. In addition, we provide an overview of the epigenetic changes and their role in the maternal-foetal transmission of chronic diseases. Overall, the knowledge of epigenetic modifications induced by an adverse intrauterine and perinatal environment could shed light on the potential pathophysiological mechanisms of long-term disease development in the offspring and provide useful tools for their prevention.

Glucocorticoid receptor (NR3C1) gene polymorphisms are associated with age and blood parameters in Polish Caucasian nonagenarians and centenarians

INTRODUCTION

Polymorphism of the glucocorticoid receptor gene (NR3C1) may modify protein abundance or function and therefore disturb human homeostasis.

METHODS

Variant frequencies of the three NR3C1 polymorphisms, rs2963154, rs10515522 and rs2918418, selected in silico as associated with longevity, was analyzed in 552 DNA samples from 95 to 106-year-old individuals and in 284 samples of cord blood DNA from newborns.

RESULTS

Frequencies of the TT genotypes of rs2963154 and rs10515522, and of the rs291841 CC genotype, were higher in the long-lived study subjects (p = 0.002, p = 0.016 and p = 0.028, respectively). In the long-lived cohort, the rs2963154 CC genotype was associated with higher concentrations of total (p = 0.007) and high-density cholesterol (p = 0.039). The rs10515522 CC genotype was associated with a higher concentration of total cholesterol (p = 0.049). The rs2918418 GG genotype was associated with higher concentrations of total (p = 0.03) and low-density cholesterol (p = 0.03). None of the polymorphisms was associated with fasting glucose, C-reactive protein levels and white blood count, prevalence of diabetes, stroke, myocardial infarction, or cognitive function. However, carriers of the rs10515522 minor allele had significantly better survival rates than carriers of other genotypes.

CONCLUSION

NR3C1 polymorphisms modify cholesterol levels, and may affect the survival rates of individuals in their tenth and eleventh decades of life.

Identification of Disease Susceptibility Alleles in the Next Generation Sequencing Era

The development of next generation sequencing (NGS) technologies has transformed the study of human genetic variation. In less than a decade, NGS has facilitated the discovery of causal mutations in both rare, monogenic diseases and common, heterogeneous disorders, leading to unprecedented improvements in disease diagnosis and treatment strategies. Given the rapid evolution of NGS platforms, it is now possible to analyze whole genomes and exomes quickly and affordably. Further, emerging NGS applications, such as single-cell sequencing, have the power to address specific issues like somatic variation, which is yielding new insights into the role of somatic mutations in cancer and late-onset diseases. Despite limitations associated with current iterations of NGS technologies, the impact of this approach on identifying disease-causing variants has been significant. This chapter provides an overview of several NGS platforms and applications and discusses how these technologies can be used in concert with experimental and computational strategies to identify variants with a causative effect on disease development and progression.

Precision medicine in diabetes prevention, classification and management

Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.

Sex differences in obesity, lipid metabolism, and inflammation-A role for the sex chromosomes?

BACKGROUND

Sex differences in obesity and related diseases are well established. Gonadal hormones are a major determinant of these sex differences. However, sex differences in body size and composition are evident prior to exposure to gonadal hormones, providing evidence for gonadal-independent contributions attributable to the XX or XY sex chromosome complement. Large-scale genetic studies have revealed male/female differences in the genetic architecture of adipose tissue amount and anatomical distribution. However, these studies have typically neglected the X and Y chromosomes.

SCOPE OF THE REVIEW

Here we discuss how the sex chromosome complement may influence obesity, lipid levels, and inflammation. Human sex chromosome anomalies such as Klinefelter syndrome (XXY), as well as mouse models with engineered alterations in sex chromosome complement, support an important role for sex chromosomes in obesity and metabolism. In particular, the Four Core Genotypes mouse model-consisting of XX mice with either ovaries or testes, and XY mice with either ovaries or testes-has revealed an effect of X chromosome dosage on adiposity, hyperlipidemia, and inflammation irrespective of male or female gonads. Mechanisms may include enhanced expression of genes that escape X chromosome inactivation.

MAJOR CONCLUSIONS

Although less well studied than effects of gonadal hormones, sex chromosomes exert independent and interactive effects on adiposity, lipid metabolism, and inflammation. In particular, the presence of two X chromosomes has been associated with increased adiposity and dyslipidemia in mouse models and in XXY men. The enhanced expression of genes that escape X chromosome inactivation may contribute, but more work is required.

Opposite Genetic Effects of CMIP Polymorphisms on the Risk of Type 2 Diabetes and Obesity: A Family-Based Study in China

C-Maf Inducing Protein (CMIP) gene polymorphisms were reported to be associated with type 2 diabetes mellitus (T2DM). Whether the association between CMIP and T2DM is mediated via obesity-related phenotypes is still unclear. We analyzed the association of CMIP rs2925979 with T2DM and a comprehensive set of obesity-related phenotypes in 1576 families ascertained from a Chinese population. These families included a total of 3444 siblings (1582 with T2DM, 963 with prediabetes, and 899 with a normal glucose level). Using multi-level mixed effects regression models, we found that each copy of CMIP rs2925979_T allele was associated with a 29% higher risk of T2DM in females (p = 9.30 × 10^-4), while it was not significantly associated with T2DM in males (p = 0.705). Meanwhile, rs2925979_T allele was associated with lower levels of body mass index (BMI), waist circumference (WC), hip circumference (HC), percentage of body fat (PBF), PBF of arms, PBF of legs, and PBF of trunk in nondiabetes females (all p < 0.05). The opposite associations of rs2925979_T allele with T2DM and obesity-related phenotypes suggest that CMIP may exert independent pleiotropic effects on T2DM and obesity-related phenotypes in females.

Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method

Genome-wide association studies (GWAS) have been shown to have the potential of explaining more of the "missing heritability" of complex human phenotypes by improving statistical approaches. Here, we applied a genetic-pleiotropy-informed conditional false discovery rate (cFDR) to capture additional polygenic effects associated with estimated glomerular filtration rate (creatinine) (eGFRcrea) and type 2 diabetes (T2D). The cFDR analysis improves the identification of pleiotropic variants by incorporating potentially shared genetic mechanisms between two related traits. The Q-Q and fold-enrichment plots were used to assess the enrichment of SNPs associated with eGFRcrea or T2D, and Manhattan plots were used for showing chromosomal locations of the significant loci detected. By applying the cFDR method, we newly identified 74 loci for eGFRcrea and 3 loci for T2D with the cFDR criterion of 0.05 compared with previous related GWAS studies. Four shared SNPs were detected to be associated with both eGFRcrea and T2D at the significant conjunction cFDR level of 0.05, and these shared SNPs had not been reported in previous studies. In addition, we used DAVID analysis to perform functional analysis of the shared SNPs' annotated genes and found their potential hidden associations with eGFRcrea and T2D. In this study, the cFDR method shows the feasibility to detect more genetic variants underlying the heritability of eGFRcrea and T2D, and the overlapping SNPs identified could be regarded as candidate loci that provide a thread of genetic mechanisms between eGFRcrea and T2D in future research.

The Rise and Fall and Rise of Linkage Analysis as a Technique for Finding and Characterizing Inherited Influences on Disease Expression

For many years, family-based studies using linkage analysis represented the primary approach for identifying disease genes. This strategy is responsible for the identification of the greatest number of genes proven to cause human disease. However, technical advancements in next generation sequencing and high throughput genotyping, coupled with the apparent simplicity of association testing, led to the rejection of family-based studies and of linkage analysis. At present, genetic association methods, using case-control comparisons, have become the exclusive approach for detecting disease-related genes, particularly those underlying common, complex diseases. In this chapter, we present a historical overview of linkage analysis, including a description of how the approach works, as well as its strengths and weaknesses. We discuss how the transition from family-based studies to population comparison association studies led to a critical loss of information with respect to genetic etiology and inheritance, and we present historical and contemporary examples of linkage analysis "success stories" in identifying genes contributing to the development of human disease. Currently, linkage analysis is re-emerging as a useful approach for identifying disease genes, determining genetic parameters, and resolving genetic heterogeneity. We posit that the combination of linkage analysis, association testing, and high throughput sequencing provides a powerful approach for identifying disease-causing genes.

Anthropometric and metabolic improvements in human type 2 diabetes after introduction of an Okinawan-based Nordic diet are not associated with changes in microbial diversity or SCFA concentrations

The Okinawan-based Nordic (O-BN) diet improves anthropometry and metabolism in type 2 diabetes mellitus (T2DM) patients. The aim of this study was to study mechanisms behind improvements by examining Enterobacteriaceae abundance, microbial diversity, and concentrations of short-chain fatty acids (SCFAs). A secondary aim was exploring if metformin treatment affects microbiota or SCFAs. Thirty T2DM patients received the O-BN diet for 12 weeks. Faecal and blood samples were collected at baseline, 12 and 28 weeks. Although patients experienced weight loss and improved metabolic parameters, there were no significant changes in Enterobacteriaceae abundance or microbial diversity. Patients on metformin displayed higher Enterobacteriaceae abundance throughout the study (p = .008, p = .038, and p = .001, respectively). Isovaleric acid was decreased after 12 weeks (p = .018). Butyric acid was decreased at follow-up (p = .007). Improved anthropometry and metabolism in T2DM after introduction of the O-BN diet is not associated with changes in Enterobacteriaceae abundance, microbial diversity or SCFA concentrations.

Novel Common Variants Associated with Obesity and Type 2 Diabetes Detected Using a cFDR Method

Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and obesity are generally focused on individual traits independently, and genetic intercommunity (common genetic contributions or the product of over correlated phenotypic world) between them are largely unknown, despite extensive data showing that these two phenotypes share both genetic and environmental risk factors. Here, we applied a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with BMI and T2D by incorporating the summary statistics from existing GWASs of these two traits. Conditional Q-Q and fold enrichment plots were used to visually demonstrate the strength of pleiotropic enrichment. Adopting a cFDR nominal significance level of 0.05, 287 loci were identified for BMI and 75 loci for T2D, 23 of which for both traits. By incorporating related traits into a conditional analysis framework, we observed significant pleiotropic enrichment between obesity and T2D. These findings may provide novel insights into the etiology of obesity and T2D, individually and jointly.

The genetic underpinnings of body fat distribution

Obesity, defined as a body mass index (BMI) ≥ 30 kg/m², has reached epidemic proportions; people who are overweight (BMI > 25 kg/m²) or obese now comprise more than 25% of the world's population. Obese individuals have a higher risk of comorbidity development including type 2 diabetes, cardiovascular disease, cancer, and fertility complications. Areas covered: The study of monogenic and syndromic forms of obesity have revealed a small number of genes key to metabolic perturbations. Further, obesity and body shape in the general population are highly heritable phenotypes. Study of obesity at the population level, through genome-wide association studies of BMI and waist-to-hip ratio (WHR), have revealed > 150 genomic loci that associate with these traits, and highlight the role of adipose tissue and the central nervous system in obesity-related traits. Studies in animal models and cell lines have helped further elucidate the potential biological mechanisms underlying obesity. In particular, these studies implicate adipogenesis and expansion of adipose tissue as key biological pathways in obesity and weight gain. Expert commentary: Further work, including a focus on integrating genetic and additional genomic data types, as well as modeling obesity-like features in vitro, will be crucial in translating genome-wide association signals to the causal mechanisms driving disease.

Design and rationale for examining neuroimaging genetics in ischemic stroke: The MRI-GENIE study

OBJECTIVE

To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study.

METHODS

MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease.

CONCLUSIONS

The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

Genomewide association study reveals a risk locus for equine metabolic syndrome in the Arabian horse

Equine obesity can cause life-threatening secondary chronic conditions, similar to those in humans and other animal species. Equine metabolic syndrome (EMS), primarily characterized by hyperinsulinemia, is often present in obese horses and ponies. Due to clinical similarities to conditions such as pituitary pars intermedia dysfunction (formerly equine Cushing's disease), conclusive diagnosis of EMS often proves challenging. Aside from changes in diet and exercise, few targeted treatments are available for EMS, emphasizing the need for early identification of at-risk individuals to enable implementation of preventative measures. A genomewide association study (GWAS) using Arabian horses with a history of severe laminitis secondary to EMS revealed significant genetic markers near a single candidate gene () that may play a role in cholesterol homeostasis. The best marker, BIEC2-263524 (chr14:69276814 T > C), was correlated with elevated insulin values and increased frequency of laminitis ( = 0.0024 and = 9.663 × 10, respectively). In a second population of Arabian horses, the BIEC2-263524 marker maintained its associations with higher modified insulin-to-glucose ratio (MIRG) values ( = 0.0056) and BCS ( = 0.0063). Screening of the predicted coding regions by sequencing identified a polymorphic guanine homopolymer and 5 haplotypes in the 3' untranslated region (UTR). An 11 guanine (11-G) allele at was correlated with elevated insulin values in the GWAS population ( = 0.0008) and, in the second population, elevated MIRG and increased BCS > 6.5 ( = 0.0055 and = 0.0162, respectively). The BIEC2-263524-C and the 3' UTR -11(G) polymorphisms were correlated at a 98% frequency, indicating strong linkage disequilibrium across this 150-kb haplotype. Assays for these markers could diagnose horses with a genetic predisposition to develop obesity. Additionally, discovery of FAM174A function may improve our understanding of the etiology of this troubling illness in the horse and warrants investigation of this locus for a role in metabolic- and obesity-related disorders of other species.

Hyperproinsulinemia in obesity and in type 2 diabetes and its relation to cardiovascular disease

Disproportionately elevated fasting levels of proinsulin immunoreactive material (PIM)relative to insulin immunoreactivity (IRI) are a well-established abnormality in type 2 diabetes. Thesignificance of this abnormality has been investigated and discussed in several studies. Areas covered: The present review focuses on the role of proinsulin and its conversion intermediates inthe development of type 2 diabetes, obesity and insulin resistance, and the potential role as a marker ofcardiovascular risk, including the most important studies in this field. Expert commentary: The composition of plasma PIM is heterogeneous comprising des(31,32)-proinsulin,intact proinsulin and small amounts of des(64,65)-proinsulin. Disproportionate hyperproinsulinemiaseems to occur early in the development and before the diagnosis of type 2 diabetes, and seemsassociated to disease progression. Obesity and insulin resistance does not influence fasting PIM/IRI levels in type 2 diabetes. Fasting PIM/IRI levels in type 2 diabetes are closely associated with the degree of impairment in insulin secretory capacity. Different type 2 diabetes alleles have been described associated with elevated PIM/IRI levels. Recent data suggests that proinsulin and its conversion intermediates may have a role as markers of increased risk of cardiovascular disease in glucose intolerance and type 2 diabetes.

Genetics of serum carotenoid concentrations and their correlation with obesity-related traits in Mexican American children

Background: Dietary intake of phytonutrients present in fruits and vegetables, such as carotenoids, is associated with a lower risk of obesity and related traits, but the impact of genetic variation on these associations is poorly understood, especially in children.Objective: We estimated common genetic influences on serum carotenoid concentrations and obesity-related traits in Mexican American (MA) children.Design: Obesity-related data were obtained from 670 nondiabetic MA children, aged 6-17 y. Serum α- and β-carotenoid concentrations were measured in ∼570 (α-carotene in 565 and β-carotene in 572) of these children with the use of an ultraperformance liquid chromatography-photodiode array. We determined heritabilities for both carotenoids and examined their genetic relation with 10 obesity-related traits [body mass index (BMI), waist circumference (WC), high-density lipoprotein (HDL) cholesterol, triglycerides, fat mass (FM), systolic and diastolic blood pressure, fasting insulin and glucose, and homeostasis model assessment of insulin resistance] by using family data and a variance components approach. For these analyses, carotenoid values were inverse normalized, and all traits were adjusted for significant covariate effects of age and sex.Results: Carotenoid concentrations were highly heritable and significant [α-carotene: heritability (h²) = 0.81, P = 6.7 × 10^-11; β-carotene: h² = 0.90, P = 3.5 × 10^-15]. After adjusting for multiple comparisons, we found significant (P ≤ 0.05) negative phenotypic correlations between carotenoid concentrations and the following traits: BMI, WC, FM, and triglycerides (range: α-carotene = -0.19 to -0.12; β-carotene = -0.24 to -0.13) and positive correlations with HDL cholesterol (α-carotene = 0.17; β-carotene = 0.24). However, when the phenotypic correlations were partitioned into genetic and environmental correlations, we found marginally significant (P = 0.051) genetic correlations only between β-carotene and BMI (-0.27), WC (-0.30), and HDL cholesterol (0.31) after accounting for multiple comparisons. None of the environmental correlations were significant.Conclusions: The findings from this study suggest that the serum carotenoid concentrations were under strong additive genetic influences based on variance components analyses, and that the common genetic factors may influence β-carotene and obesity and lipid traits in MA children.

Polymorphisms in PDLIM5 gene are associated with alcohol dependence, type 2 diabetes, and hypertension

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in alcohol dependence (AD), bipolar disorder, and major depressive disorder; however, no study has identified shared genetic variants within PDLIM5 gene among AD, type 2 diabetes (T2D), and hypertension. This study investigated the association of 72 single nucleotide polymorphism (SNPs) with AD (1066 AD cases and 1278 controls) in the Study of Addiction - Genetics and Environment (SAGE) sample and 47 SNPs with T2D (878 cases and 2686 non-diabetic) and hypertension (825 cases and 2739 non-hypertensive) in the Marshfield sample. Multiple logistic regression models in PLINK software were used to examine the associations of genetic variants with AD, T2D, and hypertension and SNP x alcohol consumption interactions for T2D and hypertension. Twenty-five SNPs were associated with AD in the SAGE sample (p < 0.05); rs1048627 showed the strongest association with AD (p = 5.53 × 10^-4). Of the 25 SNPs, 5 SNPs showed associations with both AD in the SAGE sample and T2D in the Marshfield sample (top SNP rs11097432 with p = 0.00107 for T2D and p = 0.0483 for AD) while 6 SNPs showed associations with both AD in the SAGE sample and hypertension in the Marshfield sample (top SNP rs12500426 with p = 0.0119 for hypertension and p = 1.51 × 10^-3 for AD). SNP (rs6532496) showed significant interaction with alcohol consumption for hypertension. Our results showed that several genetic variants in PDLIM5 gene influence AD, T2D and hypertension. These findings offer the potential for new insights into the pathogenesis of AD, T2D, and hypertension.

Molecular Mechanisms of Obesity-Induced Osteoporosis and Muscle Atrophy

Obesity and osteoporosis are two alarming health disorders prominent among middle and old age populations, and the numbers of those affected by these two disorders are increasing. It is estimated that more than 600 million adults are obese and over 200 million people have osteoporosis worldwide. Interestingly, both of these abnormalities share some common features including a genetic predisposition, and a common origin: bone marrow mesenchymal stromal cells. Obesity is characterized by the expression of leptin, adiponectin, interleukin 6 (IL-6), interleukin 10 (IL-10), monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-α), macrophage colony stimulating factor (M-CSF), growth hormone (GH), parathyroid hormone (PTH), angiotensin II (Ang II), 5-hydroxy-tryptamine (5-HT), Advance glycation end products (AGE), and myostatin, which exert their effects by modulating the signaling pathways within bone and muscle. Chemical messengers (e.g., TNF-α, IL-6, AGE, leptins) that are upregulated or downregulated as a result of obesity have been shown to act as negative regulators of osteoblasts, osteocytes and muscles, as well as positive regulators of osteoclasts. These additive effects of obesity ultimately increase the risk for osteoporosis and muscle atrophy. The aim of this review is to identify the potential cellular mechanisms through which obesity may facilitate osteoporosis, muscle atrophy and bone fractures.

Interactions between host genetics and gut microbiome in diabetes and metabolic syndrome

BACKGROUND

Diabetes, obesity, and the metabolic syndrome are multifactorial diseases dependent on a complex interaction of host genetics, diet, and other environmental factors. Increasing evidence places gut microbiota as important modulators of the crosstalk between diet and development of obesity and metabolic dysfunction. In addition, host genetics can have important impact on the composition and function of gut microbiota. Indeed, depending on the genetic background of the host, diet and other environmental factors may produce different changes in gut microbiota, have different impacts on host metabolism, and create different interactions between the microbiome and the host.

SCOPE OF REVIEW

In this review, we highlight how appropriate animal models can help dissect the complex interaction of host genetics with the gut microbiome and how diet can lead to different degrees of weight gain, levels of insulin resistance, and metabolic outcomes, such as diabetes, in different individuals. We also discuss the challenges of identifying specific disease-associated microbiota and the limitations of simple metrics, such as phylogenetic diversity or the ratio of Firmicutes to Bacteroidetes.

MAJOR CONCLUSIONS

Understanding these complex interactions will help in the development of novel treatments for microbiome-related metabolic diseases. This article is part of a special issue on microbiota.

Does bilirubin protect against developing diabetes mellitus?

After 25 years of evaluating bilirubin as a possible protective agent in neonatal and cardiovascular disease, interest has moved on to a exploring a possible protective role in diabetes mellitus (DM). This review finds conflicting prospective data for a protective relationship though there are retrospective, case-controlled data, that can only show association, which is not causality. Only prospective studies can show causality. Also, it would appear that the underlying biochemical assumptions do not readily translate from the animal to the human setting. Given that many factors impact on circulating bilirubin levels, it is not surprising that a clear-cut answer is not available; the jury is still out. Any relationship between DM and bilirubin might relate to intermediates in bilirubin metabolism, including relationships involving the genes for the enzymes participating in those steps. Nevertheless, the pursuit of bilirubin in disease causation is opening new avenues for research and if it is established that serum bilirubin can predict risks, much will have been achieved. The answer may have to come from molecular genetic analyses.

Gene-specific DNA methylation profiles and LINE-1 hypomethylation are associated with myocardial infarction risk

Background

DNA methylation profiles are responsive to environmental stimuli and metabolic shifts. This makes DNA methylation a potential biomarker of environmental-related and lifestyle-driven diseases of adulthood. Therefore, we investigated if white blood cells’ (WBCs) DNA methylation profiles are associated with myocardial infarction (MI) occurrence.

Whole-genome DNA methylation was investigated by microarray analysis in 292 MI cases and 292 matched controls from the large prospective Italian European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (EPICOR study). Significant signals (false discovery rate (FDR) adjusted P < 0.05) were replicated by mass spectrometry in 317 MI cases and 262 controls from the Dutch EPIC cohort (EPIC-NL). Long interspersed nuclear element-1 (LINE-1) methylation profiles were also evaluated in both groups.

Results

A differentially methylated region (DMR) within the zinc finger and BTB domain-containing protein 12 (ZBTB12) gene body and LINE-1 hypomethylation were identified in EPICOR MI cases and replicated in the EPIC-NL sample (ZBTB12-DMR meta-analysis: effect size ± se = −0.016 ± 0.003, 95 % CI = −0.021;−0.011, P = 7.54 × 10⁻¹⁰; LINE-1 methylation meta-analysis: effect size ± se = −0.161 ± 0.040, 95 % CI = −0.239;−0.082, P = 6.01 × 10⁻⁵).

Moreover, cases with shorter time to disease had more pronounced ZBTB12-DMR hypomethylation (meta-analysis, men: effect size ± se = −0.0059 ± 0.0017, P_TREND = 5.0 × 10⁻⁴; women: effect size ± se = −0.0053 ± 0.0019, P_TREND = 6.5 × 10⁻³) and LINE-1 hypomethylation (meta-analysis, men: effect size ± se = −0.0010 ± 0.0003, P_TREND = 1.6 × 10⁻³; women: effect size ± se = −0.0008 ± 0.0004, P_TREND = 0.026) than MI cases with longer time to disease.

In the EPIC-NL replication panel, DNA methylation profiles improved case-control discrimination and reclassification when compared with traditional MI risk factors only (net reclassification improvement (95 % CI) between 0.23 (0.02–0.43), P = 0.034, and 0.89 (0.64–1.14), P < 1 × 10⁻⁵).

Conclusions

Our data suggest that specific methylation profiles can be detected in WBCs, in a preclinical condition, several years before the occurrence of MI, providing an independent signature of cardiovascular risk. We showed that prediction accuracy can be improved when DNA methylation is taken into account together with traditional MI risk factors, although further confirmation on a larger sample is warranted. Our findings support the potential use of DNA methylation patterns in peripheral blood white cells as promising early biomarkers of MI.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0164-3) contains supplementary material, which is available to authorized users.