Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes

Single cell regulatory architecture of human pancreatic islets suggests sex differences in β cell function and the pathogenesis of type 2 diabetes

Type 2 and type 1 diabetes (T2D, T1D) exhibit sex differences in insulin secretion, the mechanisms of which are unknown. We examined sex differences in human pancreatic islets from 52 donors with and without T2D combining single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), hormone secretion, and bioenergetics. In nondiabetic (ND) donors, sex differences in islet cells gene accessibility and expression predominantly involved sex chromosomes. Islets from T2D donors exhibited similar sex differences in sex chromosomes differentially expressed genes (DEGs), but also exhibited sex differences in autosomal genes. Comparing β cells from T2D vs. ND donors, gene enrichment of female β cells showed suppression in mitochondrial respiration, while male β cells exhibited suppressed insulin secretion. Thus, although sex differences in gene accessibility and expression of ND β cells predominantly affect sex chromosomes, the transition to T2D reveals sex differences in autosomes highlighting mitochondrial failure in females.

Impaired GK-GKRP interaction rather than direct GK activation worsens lipid profiles and contributes to long-term complications: a Mendelian randomization study

BACKGROUND

Glucokinase (GK) plays a key role in glucose metabolism. In the liver, GK is regulated by GK regulatory protein (GKRP) with nuclear sequestration at low plasma glucose level. Some GK activators (GKAs) disrupt GK-GKRP interaction which increases hepatic cytoplasmic GK level. Excess hepatic GK activity may exceed the capacity of glycogen synthesis with excess triglyceride formation. It remains uncertain whether hypertriglyceridemia associated with some GKAs in previous clinical trials was due to direct GK activation or impaired GK-GKRP interaction.

METHODS

Using publicly available genome-wide association study summary statistics, we selected independent genetic variants of GCKR and GCK associated with fasting plasma glucose (FPG) as instrumental variables, to mimic the effects of impaired GK-GKRP interaction and direct GK activation, respectively. We applied two-sample Mendelian Randomization (MR) framework to assess their causal associations with lipid-related traits, risks of metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular diseases. We verified these findings in one-sample MR analysis using individual-level statistics from the Hong Kong Diabetes Register (HKDR).

RESULTS

Genetically-proxied impaired GK-GKRP interaction increased plasma triglycerides, low-density lipoprotein cholesterol and apolipoprotein B levels with increased odds ratio (OR) of 14.6 (95% CI 4.57-46.4) per 1 mmol/L lower FPG for MASLD and OR of 2.92 (95% CI 1.78-4.81) for coronary artery disease (CAD). Genetically-proxied GK activation was associated with decreased risk of CAD (OR 0.69, 95% CI 0.54-0.88) and not with dyslipidemia. One-sample MR validation in HKDR showed consistent results.

CONCLUSIONS

Impaired GK-GKRP interaction, rather than direct GK activation, may worsen lipid profiles and increase risks of MASLD and CAD. Development of future GKAs should avoid interfering with GK-GKRP interaction.

Genetic susceptibility to breast cancer increases the risk of neutropenia and agranulocytosis: insights from Mendelian randomization

PURPOSE

The causal relationship between breast cancer and its estrogen receptor (ER) subtypes and neutropenia and agranulocytosis is unclear.

METHODS

In two-sample Mendelian randomization (MR), we used inverse variance weighting (IVW), Bayesian weighted MR (BWMR), MR-Egger, weighted median, simple mode, and weighted mode methods to analyze causality for ER-positive breast cancer, ER-negative breast cancer, overall breast cancer, and drug-induced neutropenia and agranulocytosis. To validate the results, we performed the analysis again using GWAS data on neutropenia from different databases. In multivariable MR (MVMR), we assessed the independent effects of ER-positive and ER-negative breast cancer on causality.

RESULTS

Two-sample MR analysis showed a causal relationship between ER-positive breast cancer (IVW odds ratio (OR) = 1.319, P = 7.580 × 10-10), ER-negative breast cancer (OR = 1.285, P = 1.263 × 10-4), overall breast cancer (OR = 1.418, P = 2.123 × 10-13), and drug-induced neutropenia and a causal relationship between ER-positive breast cancer (OR = 1.349, P = 1.402 × 10-7), ER-negative breast cancer (OR = 1.235, P = 7.615 × 10-3), overall breast cancer (OR = 1.429, P = 9.111 × 10-10), and neutropenia. Similarly, ER-positive breast cancer (OR = 1.213, P = 5.350 × 10-8), ER-negative breast cancer (OR = 1.179, P = 1.300 × 10-3), and overall breast cancer (OR = 1.275, P = 8.642 × 10-11) also had a causal relationship with agranulocytosis. MVMR analysis showed that ER-positive breast cancer remained causally associated with drug-induced neutropenia (OR = 1.233, P = 4.188 × 10-4), neutropenia (OR = 1.283, P = 6.363 × 10-4), and agranulocytosis (OR = 1.142, P = 4.549 × 10-3). Heterogeneity analysis and pleiotropy test showed that our results were reliable.

CONCLUSION

Our study provides genetic evidence for a causal association between breast cancer and its estrogen receptor subtypes and neutropenia. In clinical practice, in addition to focusing on therapeutic factors, additional attention should be given to breast cancer patients to avoid severe neutropenia.

Relation Between Obesity and Type 2 Diabetes: Evolutionary Insights, Perspectives and Controversies

PURPOSE OF REVIEW

Since the mid-twentieth century, obesity and its related comorbidities, notably insulin resistance (IR) and type 2 diabetes (T2D), have surged. Nevertheless, their underlying mechanisms remain elusive. Evolutionary medicine (EM) sheds light on these issues by examining how evolutionary processes shape traits and diseases, offering insights for medical practice. This review summarizes the pathogenesis and genetics of obesity-related IR and T2D. Subsequently, delving into their evolutionary connections. Addressing limitations and proposing future research directions aims to enhance our understanding of these conditions, paving the way for improved treatments and prevention strategies.

RECENT FINDINGS

Several evolutionary hypotheses have been proposed to unmask the origin of obesity-related IR and T2D, e.g., the "thrifty genotype" hypothesis suggests that certain "thrifty genes" that helped hunter-gatherer populations efficiently store energy as fat during feast-famine cycles are now maladaptive in our modern obesogenic environment. The "drifty genotype" theory suggests that if thrifty genes were advantageous, they would have spread widely, but proposes genetic drift instead. The "behavioral switch" and "carnivore connection" hypotheses propose insulin resistance as an adaptation for a brain-dependent, low-carbohydrate lifestyle. The thrifty phenotype theory suggests various metabolic outcomes shaped by genes and environment during development. However, the majority of these hypotheses lack experimental validation. Understanding why ancestral advantages now predispose us to diseases may aid in drug development and prevention of disease. EM helps us to understand the evolutionary relation between obesity-related IR and T2D. But still gaps and contradictions persist. Further interdisciplinary research is required to elucidate complete mechanisms.

Causal relationship between type 2 diabetes mellitus and aortic dissection: insights from two-sample Mendelian randomization and mediation analysis

Objective

Some evidence suggests a reduced prevalence of type 2 diabetes mellitus (T2DM) in patients with aortic dissection (AD), a catastrophic cardiovascular illness, compared to general population. However, the conclusions were inconsistent, and the causal relationship between T2DM and AD remains unclear.

Methods

In this study, we aimed to explore the causal relationship between T2DM and AD using bidirectional Mendelian randomization (MR) analysis. Mediation MR analysis was conducted to explore and quantify the possible mediation effects of 1400 metabolites in T2DM and AD.

Results

The results of 26 datasets showed no causal relationship between T2DM and AD (P>0.05). Only one dataset (ebi-a-GCST90006934) showed that T2DM was a protective factor for AD (I9-AORTDIS) (OR=0.815, 95%CI: 0.692-0.960, P=0.014), and did not show horizontal pleiotropy (P=0.808) and heterogeneity (P=0.525). Vanillic acid glycine plays a mediator in the causal relationship between T2DM and AD. The mediator effect for vanillic acid glycine levels was -0.023 (95%CI: -0.066-0.021).

Conclusion

From the perspective of MR analysis, there might not be a causal relationship between T2DM and AD, and T2DM might not be a protective factor for AD. If a causal relationship does exist between T2DM and AD, with T2DM serving as a protective factor, vanillic acid glycine may act as a mediator and enhance such a protective effect.

SUCNR1 regulates insulin secretion and glucose elevates the succinate response in people with prediabetes

Pancreatic β-cell dysfunction is a key feature of type 2 diabetes, and novel regulators of insulin secretion are desirable. Here we report that the succinate receptor (SUCNR1) is expressed in β-cells and is up-regulated in hyperglycemic states in mice and humans. We found that succinate acts as a hormone-like metabolite and stimulates insulin secretion via a SUCNR1-Gq-PKC-dependent mechanism in human β-cells. Mice with β-cell-specific Sucnr1 deficiency exhibit impaired glucose tolerance and insulin secretion on a high-fat diet, indicating that SUCNR1 is essential for preserving insulin secretion in diet-induced insulin resistance. Patients with impaired glucose tolerance show an enhanced nutritional-related succinate response, which correlates with the potentiation of insulin secretion during intravenous glucose administration. These data demonstrate that the succinate/SUCNR1 axis is activated by high glucose and identify a GPCR-mediated amplifying pathway for insulin secretion relevant to the hyperinsulinemia of prediabetic states.

Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, we examined multiple variants that influence SLC30A8 allele-specific expression. Epigenomic mapping has previously identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighboring genes. Here, we show that deletion of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowers the expression of SLC30A8 and several neighboring genes and improves glucose-stimulated insulin secretion. While downregulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21, or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

The genetic causal relationship between type 2 diabetes, glycemic traits and venous thromboembolism, deep vein thrombosis, pulmonary embolism: a two-sample Mendelian randomization study

OBJECTIVE

To investigate the genetic underpinnings of the association between type 2 diabetes (T2D), glycemic indicators such as fasting glucose (FG), fasting insulin (FI), and glycated hemoglobin (GH), and venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE), thereby contributing novel insights to the scholarly discourse within this domain.

METHODS

Genome-wide association study (GWAS) summary data pertaining to exposures (T2D, FG, FI, GH) and outcomes (VTE, DVT, PE) were acquired from the IEU Open GWAS database, encompassing participants of European descent, including both male and female individuals. Two-sample Mendelian randomization (MR) analyses were conducted utilizing the TwoSampleMR and MRPRESSO packages within the R programming environment. The primary analytical approach employed was the random-effects inverse variance weighted (IVW) method. Heterogeneity was assessed via Cochran's Q statistic for MR-IVW and Rucker's Q statistic for MR-Egger. Horizontal pleiotropy was evaluated using the intercept test of MR Egger and MR pleiotropy residual sum and outlier (MR-PRESSO) analysis, with the latter also employed for outlier detection. Additionally, a "Leave one out" analysis was conducted to ascertain the influence of individual single nucleotide polymorphisms (SNPs) on MR results.

RESULTS

The random-effects IVW analysis revealed a negative genetic causal association between T2D) and VTE (P = 0.008, Odds Ratio [OR] 95% confidence interval [CI] = 0.896 [0.827-0.972]), as well as between FG and VTE (P = 0.002, OR 95% CI = 0.655 [0.503-0.853]), GH and VTE (P = 0.010, OR 95% CI = 0.604 [0.412-0.884]), and GH and DVT (P = 0.002, OR 95% CI = 0.413 [0.235-0.725]). Conversely, the random-effects IVW analysis did not detect a genetic causal relationship between FI and VTE (P > 0.05), nor between T2D, FG, or FI and DVT (P > 0.05), or between T2D, FG, FI, or GH and PE (P > 0.05). Both the Cochran's Q statistic for MR-IVW and Rucker's Q statistic for MR-Egger indicated no significant heterogeneity (P > 0.05). Moreover, the intercept tests of MR Egger and MR-PRESSO suggested the absence of horizontal pleiotropy (P > 0.05). MR-PRESSO analysis identified no outliers, while the "Leave one out" analysis underscored that the MR analysis was not influenced by any single SNP.

CONCLUSION

Our investigation revealed that T2D, FG, and GH exhibit negative genetic causal relationships with VTE at the genetic level, while GH demonstrates a negative genetic causal relationship with DVT at the genetic level. These findings furnish genetic-level evidence warranting further examination of VTE, DVT, and PE, thereby making a contribution to the advancement of related research domains.

Association between high-density lipoprotein cholesterol and type 2 diabetes mellitus: dual evidence from NHANES database and Mendelian randomization analysis

Background

Low levels of high-density lipoprotein cholesterol (HDL-C) are commonly seen in patients with type 2 diabetes mellitus (T2DM). However, it is unclear whether there is an independent or causal link between HDL-C levels and T2DM. This study aims to address this gap by using the The National Health and Nutrition Examination Survey (NHANES) database and Mendelian randomization (MR) analysis.

Materials and methods

Data from the NHANES survey (2007-2018) with 9,420 participants were analyzed using specialized software. Logistic regression models and restricted cubic splines (RCS) were used to assess the relationship between HDL-C and T2DM incidence, while considering covariates. Genetic variants associated with HDL-C and T2DM were obtained from genome-wide association studies (GWAS), and Mendelian randomization (MR) was used to evaluate the causal relationship between HDL-C and T2DM. Various tests were conducted to assess pleiotropy and outliers.

Results

In the NHANES study, all groups, except the lowest quartile (Q1: 0.28-1.09 mmol/L], showed a significant association between HDL-C levels and reduced T2DM risk (all P < 0.001). After adjusting for covariates, the Q2 [odds ratio (OR) = 0.67, 95% confidence interval (CI): (0.57, 0.79)], Q3 [OR = 0.51, 95% CI: (0.40, 0.65)], and Q4 [OR = 0.29, 95% CI: (0.23, 0.36)] groups exhibited average reductions in T2DM risk of 23%, 49%, and 71%, respectively. In the sensitivity analysis incorporating other lipid levels, the Q4 group still demonstrates a 57% reduction in the risk of T2DM. The impact of HDL-C levels on T2DM varied with age (P for interaction = 0.006). RCS analysis showed a nonlinear decreasing trend in T2DM risk with increasing HDL-C levels (P = 0.003). In the MR analysis, HDL-C levels were also associated with reduced T2DM risk (OR = 0.69, 95% CI = 0.52-0.82; P = 1.41 × 10-13), and there was no evidence of pleiotropy or outliers.

Conclusion

This study provides evidence supporting a causal relationship between higher HDL-C levels and reduced T2DM risk. Further research is needed to explore interventions targeting HDL-C levels for reducing T2DM risk.

Association between nonalcoholic fatty liver disease and type 2 diabetes: A bidirectional two-sample mendelian randomization study

OBJECTIVE

The aim of this study was to explore the mutually causal relationship between NAFLD and type 2 diabetes.

METHODS

Based on the data obtained from GWAS, this study employed bidirectional two-sample MR analysis to investigate the causal relationship between NAFLD and type 2 diabetes, and also examined the causal relationship between liver fat accumulation and type 2 diabetes as well as the relationship between NAFLD and FPG, IR.

RESULTS

In MR analysis of NAFLD and type 2 diabetes, when NAFLD as an exposure and type 2 diabetes as a result, the OR (95 % CI) was 1.10890 (1.00135-1.22801); in the reverse analysis, the OR value was not statistically significant. In MR analysis of NAFLD, FPG and IR, there was no statistical significance in both directions. In MR analysis of liver fat accumulation and type 2 diabetes, when liver fat as an exposure and type 2 diabetes as a result, the OR (95 % CI) was 1.17516 (1.02054-1.35321); in the reverse analysis, the OR value (95 % CI) was 1.06283 (1.02879-1.09799).

CONCLUSION

There is a unidirectional causal relationship between NAFLD and type 2 diabetes. Furthermore, a bidirectional causal relationship exists between liver fat accumulation and type 2 diabetes.

Exhaustive Variant Interaction Analysis using Multifactor Dimensionality Reduction

One of the main goals of human genetics is to understand the connections between genomic variation and the predisposition to develop a complex disorder. These disease-variant associations are usually studied in a single independent manner, disregarding the possible effect derived from the interaction between genomic variants. In particular, in a background of complex diseases, these interactions can be directly linked to the disorder and may play an important role in disease development. Although their study has been suggested to help to complete the understanding of the genetic bases of complex diseases, this still represents a big challenge due to large computing demands. Here, we have taken advantage of High-Performance Computing technologies to tackle this problem using a combination of machine learning methods and statistical approaches. As a result, we have created a containerized framework that uses Multifactor Dimensionality Reduction to detect pairs of variants associated with Type 2 Diabetes (T2D). This methodology has been tested in the Northwestern University NUgene project cohort using a dataset of 1,883,192 variant pairs with a certain degree of association with T2D. Out of the pairs studied, we have identified 104 significant pairs, two of which exhibit a potential functional relationship with T2D.

Type 1 diabetes, glycemic traits, and risk of dental caries: a Mendelian randomization study

Background: Regarding past epidemiological studies, there has been disagreement over whether type 1 diabetes (T1DM) is one of the risk factors for dental caries. The purpose of this study was to determine the causative links between genetic susceptibility to T1DM, glycemic traits, and the risk of dental caries using Mendelian randomization (MR) approaches. Methods: Summary-level data were collected on genome-wide association studies (GWAS) of T1DM, fasting glucose (FG), glycated hemoglobin (HbA1c), fasting insulin (FI), and dental caries. MR was performed using the inverse-variance weighting (IVW) method, and sensitivity analyses were conducted using the MR-Egger method, weighted median, weighted mode, replication cohort, and multivariable MR conditioning on potential mediators. Results: The risk of dental caries increased as a result of genetic susceptibility to T1DM [odds ratio (OR) = 1.044; 95% confidence interval (CI) = 1.015-1.074; p = 0.003], with consistent findings in the replication cohort. The relationship between T1DM and dental caries was stable when adjusted for BMI, smoking, alcohol intake, and type 2 diabetes (T2DM) in multivariable MR. However, no significant correlations between the risk of dental caries and FG, HbA1c, or FI were found. Conclusion: These results indicate that T1DM has causal involvement in the genesis of dental caries. Therefore, periodic reinforcement of oral hygiene instructions must be added to the management and early multidisciplinary intervention of T1DM patients, especially among adolescents and teenagers, who are more susceptible to T1DM.

Transcriptional coactivation by EHMT2 restricts glucocorticoid-induced insulin resistance in a study with male mice

The classical dogma of glucocorticoid-induced insulin resistance is that it is caused by the transcriptional activation of hepatic gluconeogenic and insulin resistance genes by the glucocorticoid receptor (GR). Here, we find that glucocorticoids also stimulate the expression of insulin-sensitizing genes, such as Irs2. The transcriptional coregulator EHMT2 can serve as a transcriptional coactivator or a corepressor. Using male mice that have a defective EHMT2 coactivation function specifically, we show that glucocorticoid-induced Irs2 transcription is dependent on liver EHMT2's coactivation function and that IRS2 play a key role in mediating the limitation of glucocorticoid-induced insulin resistance by EHMT2's coactivation. Overall, we propose a model in which glucocorticoid-regulated insulin sensitivity is determined by the balance between glucocorticoid-modulated insulin resistance and insulin sensitizing genes, in which EHMT2 coactivation is specifically involved in the latter process.

Mendelian randomization indicates a causal contribution of type 2 diabetes to retinal vein occlusion

Background

Retinal vein occlusion (RVO) is a common retinal vascular disease that can cause severe visual impairment. Many observational studies have shown that type 2 diabetes (T2DM) is associated with RVO, but it remains unknown if the association is causal. The present study aimed to perform Mendelian randomization (MR) analyses to evaluate the causal contribution of genetically predicted T2DM to RVO.

Methods

We obtained summary-level data from a genome-wide association study meta-analysis including 48,286 cases and 250,671 controls for T2DM and from a genome wide association study of 372 cases and 182,573 controls in the FinnGen project for RVO. To verify the robustness of the results, an independent validation dataset for T2DM (12,931 cases and 57,196 controls) was used. In addition to the main MR analysis using the inverse variance weighted (fixed effect) approach, sensitivity analyses and multivariable MR adjusting for common risk factors of RVO were conducted.

Results

Genetically predicted T2DM was found to be causally associated with RVO risk (odds ratio (OR)=2.823, 95% confidence interval (CI): 2.072-3.847, P=4.868×10^-11). This association was supported by sensitivity analyses using the weighted median (OR=2.415, 95% CI: 1.411-4.132, P=1.294×10^-3), weighted mode (OR=2.370, 95% CI: 1.321-4.252, P=5.159×10^-3), maximum likelihood (OR=2.871, 95% CI: 2.100-3.924, P=3.719×10^-11), MR-PRESSO (OR=2.823, 95% CI: 2.135-3.733, P=5.150×10^-10), and MR-Egger (OR=2.441, 95% CI: 1.149-5.184, P=2.335×10^-2) methods. In addition, this association persisted in multivariable MR after accounting for common RVO risk factors (OR=1.748, 95% CI: 1.238-2.467, P=1.490×10^-3). The MR analyses using the validation dataset obtained consistent results.

Conclusion

This study indicates that genetically predicted T2DM may have a causal contribution to RVO. Future studies are required to elucidate the underlying mechanisms.

Rare and Common Variants in GALNT3 May Affect Bone Mass Independently of Phosphate Metabolism

Anabolic treatment options for osteoporosis remain limited. One approach to discovering novel anabolic drug targets is to identify genetic causes of extreme high bone mass (HBM). We investigated a pedigree with unexplained HBM within the UK HBM study, a national cohort of probands with HBM and their relatives. Whole exome sequencing (WES) in a family with HBM identified a rare heterozygous missense variant (NM_004482.4:c.1657C > T, p.Arg553Trp) in GALNT3, segregating appropriately. Interrogation of data from the UK HBM study and the Anglo-Australasian Osteoporosis Genetics Consortium (AOGC) revealed an unrelated individual with HBM with another rare heterozygous variant (NM_004482.4:c.831 T > A, p.Asp277Glu) within the same gene. In silico protein modeling predicted that p.Arg553Trp would disrupt salt-bridge interactions, causing instability of GALNT3, and that p.Asp277Glu would disrupt manganese binding and consequently GALNT3 catalytic function. Bi-allelic loss-of-function GALNT3 mutations alter FGF23 metabolism, resulting in hyperphosphatemia and causing familial tumoral calcinosis (FTC). However, bone mineral density (BMD) in FTC cases, when reported, has been either normal or low. Common variants in the GALNT3 locus show genome-wide significant associations with lumbar, femoral neck, and total body BMD. However, no significant associations with BMD are observed at loci coding for FGF23, its receptor FGFR1, or coreceptor klotho. Mendelian randomization analysis, using expression quantitative trait loci (eQTL) data from primary human osteoblasts and genome-wide association studies data from UK Biobank, suggested increased expression of GALNT3 reduces total body, lumbar spine, and femoral neck BMD but has no effect on phosphate concentrations. In conclusion, rare heterozygous loss-of-function variants in GALNT3 may cause HBM without altering phosphate concentration. These findings suggest that GALNT3 may affect BMD through pathways other than FGF23 regulation, the identification of which may yield novel anabolic drug targets for osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity

OBJECTIVE

Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity.

RESEARCH DESIGN AND METHODS

Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459).

RESULTS

The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations.

CONCLUSIONS

Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.

High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease

AIMS/HYPOTHESIS

Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways.

METHODS

We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance.

RESULTS

We identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes.

CONCLUSIONS/INTERPRETATION

Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.

A genome-wide CRISPR screen identifies CALCOCO2 as a regulator of beta cell function influencing type 2 diabetes risk

Identification of the genes and processes mediating genetic association signals for complex diseases represents a major challenge. As many of the genetic signals for type 2 diabetes (T2D) exert their effects through pancreatic islet-cell dysfunction, we performed a genome-wide pooled CRISPR loss-of-function screen in a human pancreatic beta cell line. We assessed the regulation of insulin content as a disease-relevant readout of beta cell function and identified 580 genes influencing this phenotype. Integration with genetic and genomic data provided experimental support for 20 candidate T2D effector transcripts including the autophagy receptor CALCOCO2. Loss of CALCOCO2 was associated with distorted mitochondria, less proinsulin-containing immature granules and accumulation of autophagosomes upon inhibition of late-stage autophagy. Carriers of T2D-associated variants at the CALCOCO2 locus further displayed altered insulin secretion. Our study highlights how cellular screens can augment existing multi-omic efforts to support mechanistic understanding and provide evidence for causal effects at genome-wide association studies loci.

Insight into genetic, biological, and environmental determinants of sexual-dimorphism in type 2 diabetes and glucose-related traits

There is growing evidence that sex and gender differences play an important role in risk and pathophysiology of type 2 diabetes (T2D). Men develop T2D earlier than women, even though there is more obesity in young women than men. This difference in T2D prevalence is attenuated after the menopause. However, not all women are equally protected against T2D before the menopause, and gestational diabetes represents an important risk factor for future T2D. Biological mechanisms underlying sex and gender differences on T2D physiopathology are not yet fully understood. Sex hormones affect behavior and biological changes, and can have implications on lifestyle; thus, both sex-specific environmental and biological risk factors interact within a complex network to explain the differences in T2D risk and physiopathology in men and women. In addition, lifetime hormone fluctuations and body changes due to reproductive factors are generally more dramatic in women than men (ovarian cycle, pregnancy, and menopause). Progress in genetic studies and rodent models have significantly advanced our understanding of the biological pathways involved in the physiopathology of T2D. However, evidence of the sex-specific effects on genetic factors involved in T2D is still limited, and this gap of knowledge is even more important when investigating sex-specific differences during the life course. In this narrative review, we will focus on the current state of knowledge on the sex-specific effects of genetic factors associated with T2D over a lifetime, as well as the biological effects of these different hormonal stages on T2D risk. We will also discuss how biological insights from rodent models complement the genetic insights into the sex-dimorphism effects on T2D. Finally, we will suggest future directions to cover the knowledge gaps.

Evaluating the impact of glucokinase activation on risk of cardiovascular disease: a Mendelian randomisation analysis

BACKGROUND

Glucokinase activators (GKAs) are an emerging class of glucose lowering drugs that activate the glucose-sensing enzyme glucokinase (GK). Pending formal cardiovascular outcome trials, we applied two-sample Mendelian randomisation (MR) to investigate the impact of GK activation on risk of cardiovascular diseases.

METHODS

We used independent genetic variants in or around the glucokinase gene meanwhile associated with HbA_1c at genome-wide significance (P < 5 × 10^-8) in the Meta-Analyses of Glucose and Insulin-related traits Consortium study (N = 146,806; European ancestry) as instrumental variables (IVs) to mimic the effects of GK activation. We assessed the association between genetically proxied GK activation and the risk of coronary artery disease (CAD; 122,733 cases and 424,528 controls), peripheral arterial disease (PAD; 7098 cases and 206,541 controls), stroke (40,585 cases and 406,111 controls) and heart failure (HF; 47,309 cases and 930,014 controls), using genome-wide association study summary statistics of these outcomes in Europeans. We compared the effect estimates of genetically proxied GK activation with estimates of genetically proxied lower HbA_1c on the same outcomes. We repeated our MR analyses in East Asians as validation.

RESULTS

Genetically proxied GK activation was associated with reduced risk of CAD (OR 0.38 per 1% lower HbA_1c, 95% CI 0.29-0.51, P = 8.77 × 10^-11) and HF (OR 0.54 per 1% lower HbA_1c, 95% CI 0.41-0.73, P = 3.55 × 10^-5). The genetically proxied protective effects of GKA on CAD and HF exceeded those due to non-targeted HbA_1c lowering. There was no causal relationship between genetically proxied GK activation and risk of PAD or stroke. The estimates in sensitivity analyses and in East Asians were generally consistent.

CONCLUSIONS

GKAs may protect against CAD and HF which needs confirmation by long-term clinical trials.

Canary: an automated tool for the conversion of MaCH imputed dosage files to PLINK files

Background

Previous studies have demonstrated the value of re-analysing publicly available genetics data with recent analytical approaches. Publicly available datasets, such as the Women’s Health Initiative (WHI) offered by the database of genotypes and phenotypes (dbGaP), provide a wealthy resource for researchers to perform multiple analyses, including Genome-Wide Association Studies. Often, the genetic information of individuals in these datasets are stored in imputed dosage files output by MaCH; mldose and mlinfo files. In order for researchers to perform GWAS studies with this data, they must first be converted to a file format compatible with their tool of choice e.g., PLINK. Currently, there is no published tool which easily converts the datasets provided in MACH dosage files into PLINK-ready files.

Results

Herein, we present Canary a singularity-based tool which converts MaCH dosage files into PLINK-compatible files with a single line of user input at the command line. Further, we provide a detailed tutorial on preparation of phenotype files. Moreover, Canary comes with preinstalled software often used during GWAS studies, to further increase the ease-of-use of HPC systems for researchers.

Conclusions

Until now, conversion of imputed data in the form of MaCH mldose and mlinfo files needed to be completed manually. Canary uses singularity container technology to allow users to automatically convert these MaCH files into PLINK compatible files. Additionally, Canary provides researchers with a platform to conduct GWAS analysis more easily as it contains essential software needed for conducting GWAS studies, such as PLINK and Bioconductor. We hope that this tool will greatly increase the ease at which researchers can perform GWAS with imputed data, particularly on HPC environments.

Associations of the vasoactive peptides CT-proET-1 and MR-proADM with incident type 2 diabetes: results from the BiomarCaRE Consortium

BACKGROUND

Endothelin-1 (ET-1) and adrenomedullin (ADM) are commonly known as vasoactive peptides that regulate vascular homeostasis. Less recognised is the fact that both peptides could affect glucose metabolism. Here, we investigated whether ET-1 and ADM, measured as C-terminal-proET-1 (CT-proET-1) and mid-regional-proADM (MR-proADM), respectively, were associated with incident type 2 diabetes.

METHODS

Based on the population-based Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium data, we performed a prospective cohort study to examine associations of CT-proET-1 and MR-proADM with incident type 2 diabetes in 12,006 participants. During a median follow-up time of 13.8 years, 862 participants developed type 2 diabetes. The associations were examined in Cox proportional hazard models. Additionally, we performed two-sample Mendelian randomisation analyses using published data.

RESULTS

CT-proET-1 and MR-proADM were positively associated with incident type 2 diabetes. The multivariable hazard ratios (HRs) [95% confidence intervals (CI)] were 1.10 [1.03; 1.18], P = 0.008 per 1-SD increase of CT-proET-1 and 1.11 [1.02; 1.21], P = 0.016 per 1-SD increase of log MR-proADM, respectively. We observed a stronger association of MR-proADM with incident type 2 diabetes in obese than in non-obese individuals (P-interaction with BMI < 0.001). The HRs [95%CIs] were 1.19 [1.05; 1.34], P = 0.005 and 1.02 [0.90; 1.15], P = 0.741 in obese and non-obese individuals, respectively. Our Mendelian randomisation analyses yielded a significant association of CT-proET-1, but not of MR-proADM with type 2 diabetes risk.

CONCLUSIONS

Higher concentrations of CT-proET-1 and MR-proADM are associated with incident type 2 diabetes, but our Mendelian randomisation analysis suggests a probable causal link for CT-proET-1 only. The association of MR-proADM seems to be modified by body composition.

Systemic Lupus Erythematosus and Cardiovascular Disease: A Mendelian Randomization Study

Background

Previous studies have shown that patients with systemic lupus erythematosus (SLE) tend to have a higher risk of cardiovascular disease (CVD), but the potential causal relationship between genetic susceptibility to SLE and CVD risk is not clear. This study systematically investigated the potential association between genetically determined SLE and the risk of CVD.

Methods

The genetic tools were obtained from genome-wide association studies of SLE and CVD, with no overlap between their participating populations. Mendelian randomization (MR) analysis was performed using inverse variance weighting as the primary method. Simultaneously, a series of repeated analyses, sensitivity analyses, and instrumental variable strength evaluations were performed to verify the reliability of our results.

Results

MR analysis showed that genetic susceptibility to SLE was associated with a higher risk of heart failure (OR=1.025, 95% CI [1.009-1.041], P=0.002), ischemic stroke (OR=1.020, 95% CI [1.005-1.034], P=0.009), and venous thromboembolism (OR=1.001, 95% CI [1.000-1.002], P=0.014). However, genetic susceptibility to SLE was negatively correlated with the risk of type 2 diabetes (OR=0.968, 95% CI [0.947-0.990], P=0.004). Sensitivity analysis found no evidence of horizontal pleiotropy or heterogeneity.

Conclusion

Our MR study explored the causal role of SLE in the etiology of CVD, which would help improve our understanding of the basic disease mechanisms of SLE and provide comprehensive CVD assessment and treatment for SLE patients.

Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight

BACKGROUND

Higher birthweight is associated with higher adult body mass index (BMI). Alleles that predispose to greater adult adiposity might act in fetal life to increase fetal growth and birthweight. Whether there are fetal effects of recently identified adult metabolically favorable adiposity alleles on birthweight is unknown.

AIM

We aimed to test the effect on birthweight of fetal genetic predisposition to higher metabolically favorable adult adiposity and compare that with the effect of fetal genetic predisposition to higher adult BMI.

METHODS

We used published genome wide association study data (n = upto 406 063) to estimate fetal effects on birthweight (adjusting for maternal genotype) of alleles known to raise metabolically favorable adult adiposity or BMI. We combined summary data across single nucleotide polymorphisms (SNPs) with random effects meta-analyses. We performed weighted linear regression of SNP-birthweight effects against SNP-adult adiposity effects to test for a dose-dependent association.

RESULTS

Fetal genetic predisposition to higher metabolically favorable adult adiposity and higher adult BMI were both associated with higher birthweight (3 g per effect allele (95% CI: 1-5) averaged over 14 SNPs; P = 0.002; 0.5 g per effect allele (95% CI: 0-1) averaged over 76 SNPs; P = 0.042, respectively). SNPs with greater effects on metabolically favorable adiposity tended to have greater effects on birthweight (R2 = 0.2912, P = 0.027). There was no dose-dependent association for BMI (R2 = -0.0019, P = 0.602).

CONCLUSIONS

Fetal genetic predisposition to both higher adult metabolically favorable adiposity and BMI is associated with birthweight. Fetal effects of metabolically favorable adiposity-raising alleles on birthweight are modestly proportional to their effects on future adiposity, but those of BMI-raising alleles are not.

Diabetes risk loci-associated pathways are shared across metabolic tissues

Aims/hypothesis

Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants.

Methods

In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms.

Results

One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated.

Conclusion

Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08587-5.

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution

Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotating biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpret non-coding regions. Here, we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only two self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of the unlabelled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against the fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based DNA language model. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution.

Promotion of data sharing needs more than an emergency: An analysis of trends across clinical trials registered on the International Clinical Trials Registry Platform

BACKGROUND: A growing body of evidence shows that sharing health research data with other researchers for secondary analyses can contribute to better health. This is especially important in the context of a public health emergency when stopping a pandemic depends on accelerating science.

METHODS: We analysed the information on data sharing collected by the 18 clinical trial registries included in the WHO International Clinical Trials Registry Platform (ICTRP) to understand the reporting of data sharing plans and which studies were and were not planning to share data. Data on sponsor and funder organisations, country of recruitment, registry, and condition of study were standardised to compare the sharing of information and data across these facets. This represents the first ever comprehensive study of the complete data set contained in ICTRP.

RESULTS: Across 132,545 studies registered between January 2019 and December 2020, 11.2% of studies stated that individual patient data (IPD) would be shared. Plans to share IPD varied across the 18 contributing registries– information on data sharing was missing in >95% of study records across 7/18 registries. In the 26,851 (20.3%) studies that were funded or sponsored by a commercial entity, intention to share IPD was similar to those that were not (11.5% vs 11.2%). Intention to share IPD was most common in studies recruiting across both high-income and low- or middle-income countries (21.4%) and in those recruiting in Sub-Saharan Africa (50.3%). Studies of COVID-19 had similar levels of data sharing to studies of other non-pandemic diseases in 2020 (13.7% vs 11.7%).

CONCLUSIONS: Rates of planned IPD sharing vary between clinical trial registries and economic regions, and are similar whether commercial or non-commercial agencies are involved. Despite many calls to action, plans to share IPD have not increased significantly and remain below 14% for diseases causing public health emergencies.

Recessive Genome-Wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 case subjects and 279,507 control subjects from 7 European-ancestry cohorts, including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five variants had minor allele frequency of <5% and were each associated with more than a doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19; P = 1 × 10-16) and a stronger effect in men than in women (for interaction, P = 7 × 10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL cholesterol and a 20% increase in triglycerides; colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared with GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

Deep Resequencing of 9 Candidate Genes Identifies a Role for ARAP1 and IGF2BP2 in Modulating Insulin Secretion Adjusted for Insulin Resistance in Obese Southern Europeans

Type 2 diabetes is characterized by impairment in insulin secretion, with an established genetic contribution. We aimed to evaluate common and low-frequency (1–5%) variants in nine genes strongly associated with insulin secretion by targeted sequencing in subjects selected from the extremes of insulin release measured by the disposition index. Collapsing data by gene and/or function, the association between disposition index and nonsense variants were significant, also after adjustment for confounding factors (OR = 0.25, 95% CI = 0.11–0.59, p = 0.001). Evaluating variants individually, three novel variants in ARAP1, IGF2BP2 and GCK, out of eight reaching significance singularly, remained associated after adjustment. Constructing a genetic risk model combining the effects of the three variants, only carriers of the ARAP1 and IGF2BP2 variants were significantly associated with a reduced probability to be in the lower, worst, extreme of insulin secretion (OR = 0.223, 95% CI = 0.105–0.473, p < 0.001). Observing a high number of normal glucose tolerance between carriers, a regression posthoc analysis was performed. Carriers of genetic risk model variants had higher probability to be normoglycemic, also after adjustment (OR = 2.411, 95% CI = 1.136–5.116, p = 0.022). Thus, in our southern European cohort, nonsense variants in all nine candidate genes showed association with better insulin secretion adjusted for insulin resistance, and we established the role of ARAP1 and IGF2BP2 in modulating insulin secretion.

Type 2 diabetes mellitus and the risk of abnormal spermatozoa: A Mendelian randomization study

Abnormal spermatozoa can not only reduce the fertilization rate, but also prolong the natural conception time and even increase the risk of spontaneous miscarriage. Diabetes mellitus (DM) has become a major global health problem, and its incidence continues to rise, while affecting an increasing number of men in their reproductive years. Type 2 Diabetes Mellitus (T2DM), accounting for about 85-95% of DM, is closely related to the development of sperm. However, the exact association between T2DM and abnormal spermatozoa remains unclear. Herein, we designed a Two-sample Mendelian randomization (MR) study to explore the causal association between T2DM and abnormal spermatozoa risk in European population data which come from the GWAS summary datasets. We selected 9 single nucleotide polymorphisms (SNPs) of T2DM (exposure data) as instrumental variables (IVs), and then retrieved the suitable abnormal spermatozoa genome-wide association study (GWAS) data of European from Ieu Open GWAS Project database which includes 915 cases and 209,006 control as the outcome data. Our results indicate that strict T2DM might not result in a higher risk of abnormal spermatozoa genetically in Europeans (OR: 1.017, 95% confidence interval (CI): 0.771-1.342, p=0.902). Our findings demonstrate that only T2DM may not explain the relatively higher risk of abnormal spermatozoa in men with it in Europeans. In subsequent studies, more comprehensive and larger samples need to be studied to reveal the relationship and potential mechanism between T2DM and abnormal spermatozoa.

Genome-Wide Association Analysis to Search for New Loci Associated with Lifelong Premature Ejaculation Risk in Chinese Male Han Population

PURPOSE

Genetic factors play an indispensable role in the pathogenesis of lifelong premature ejaculation (LPE). The susceptibility genes/SNPs that have been discovered are very limited and can only explain part of the genetic effects of LPE. Therefore, discovering more genetic polymorphisms associated with the occurrence and development of LPE will help reveal the pathogenesis of LPE.

MATERIALS AND METHODS

We conducted a genome-wide association study of LPE in 486 Chinese male Han people (cases and controls). We used Gene Titan multi-channel instrument and Axiom Analysis Suite 6.0 software for genotyping. Imputation was performed by IMPUTE2 software and the 1000 Genomes Project (Phase3) was used as reference for haplotype. Finally, logistic regression analysis was performed on all loci that passed the quality control. The odds ratio and 95% confidence interval were calculated to determine the association between each SNPs and Chinese male Han population LPE risk.

RESULTS

The results showed that a total of 33 genetic variants in 13 genes (LACTBL1, SSBP3, ACOT11, LINC02486, TMEM154, LINC01098, NONE, HCG27, HLA-C, TNFSF8, TNC, FAM53B, SULF2) have a suggestively significant genome-wide association with LPE risk (p<5×10^-6).

CONCLUSIONS

This study is the first to conduct a GWAS on LPE in Chinese male Han population 33 genetic polymorphisms have a suggestive genome-wide association with LPE risk. This study have provided data supplement for the genetic loci of LPE risk, and laid a scientific foundation for the pathogenesis and the targeted therapy of LPE.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets

Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

Genome-wide analyses of multiple obesity-related cytokines and hormones informs biology of cardiometabolic traits

BACKGROUND

A complex set of perturbations occur in cytokines and hormones in the etiopathogenesis of obesity and related cardiometabolic conditions such as type 2 diabetes (T2D). Evidence for the genetic regulation of these cytokines and hormones is limited, particularly in African-ancestry populations. In order to improve our understanding of the biology of cardiometabolic traits, we investigated the genetic architecture of a large panel of obesity- related cytokines and hormones among Africans with replication analyses in African Americans.

METHODS

We performed genome-wide association studies (GWAS) in 4432 continental Africans, enrolled from Ghana, Kenya, and Nigeria as part of the Africa America Diabetes Mellitus (AADM) study, for 13 obesity-related cytokines and hormones, including adipsin, glucose-dependent insulinotropic peptide (GIP), glucagon-like peptide-1 (GLP-1), interleukin-1 receptor antagonist (IL1-RA), interleukin-6 (IL-6), interleukin-10 (IL-10), leptin, plasminogen activator inhibitor-1 (PAI-1), resistin, visfatin, insulin, glucagon, and ghrelin. Exact and local replication analyses were conducted in African Americans (n = 7990). The effects of sex, body mass index (BMI), and T2D on results were investigated through stratified analyses.

RESULTS

GWAS identified 39 significant (P value < 5 × 10-8) loci across all 13 traits. Notably, 14 loci were African-ancestry specific. In this first GWAS for adipsin and ghrelin, we detected 13 and 4 genome-wide significant loci respectively. Stratified analyses by sex, BMI, and T2D showed a strong effect of these variables on detected loci. Eight novel loci were successfully replicated: adipsin (3), GIP (1), GLP-1 (1), and insulin (3). Annotation of these loci revealed promising links between these adipocytokines and cardiometabolic outcomes as illustrated by rs201751833 for adipsin and blood pressure and locus rs759790 for insulin level and T2D in lean individuals.

CONCLUSIONS

Our study identified genetic variants underlying variation in multiple adipocytokines, including the first loci for adipsin and ghrelin. We identified population differences in variants associated with adipocytokines and highlight the importance of stratification for discovery of loci. The high number of African-specific loci detected emphasizes the need for GWAS in African-ancestry populations, as these loci could not have been detected in other populations. Overall, our work contributes to the understanding of the biology linking adipocytokines to cardiometabolic traits.

Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar

Type 2 diabetes (T2D) is highly prevalent and is a strong contributor for cardiovascular disease. However, there is significant heterogeneity in disease pathogenesis and the risk of complications. Enormous progress has been made in our ability to catalog genetic variation associated with T2D risk and variation in disease-relevant quantitative traits. These discoveries hold the potential to shed light on tractable targets and pathways for safe and effective therapeutic development, but the promise of precision medicine has been slow to be realized. Recent studies have identified subgroups of individuals with differential risk for intermediate phenotypes (eg, lipid levels, fasting insulin, body mass index) that contribute to T2D risk, helping to account for the observed clinical heterogeneity. These "partitioned genetic risk scores" not only have the potential to identify patients at greatest risk of cardiovascular disease and rapid disease progression, but also could aid patient stratification bridging the gap toward precision medicine for T2D.

Type 1 Diabetes and the HLA Region: Genetic Association Besides Classical HLA Class II Genes

Type 1 diabetes is an autoimmune disease with rising incidence in high-income countries. Genetic and environmental predisposing factors contribute to the etiology of the disease, although their interaction is not sufficiently understood to allow for preventive action. Strongest known associations with genetic variation map to classical HLA class II genes. Because of its genetic complexity, the HLA region has been under-represented in genome-wide association studies, having potentially hindered the identification of relevant associations underlying the etiology of the disease. Here, we performed a comprehensive HLA-wide genetic association analysis of type 1 diabetes including multi-allelic and rare variants. We used high-density whole-exome sequencing data of the HLA region in the large UK Biobank dataset to apply gene-based association tests with a carefully defined type 1 diabetes phenotype (97 cases and 48,700 controls). Exon-based and single-variant association tests were used to complement the analysis. We replicated the known association of type 1 diabetes with the classical HLA-DQ gene. Tailoring the analysis toward rare variants, we additionally identified the lysine methyl transferase EHMT2 as associated. Deeper insight into genetic variation associated with disease as presented and discussed in detail here can help unraveling mechanistic details of the etiology of type 1 diabetes. More specifically, we hypothesize that genetic variation in EHMT2 could impact autoimmunity in type 1 diabetes development.

CRISPR-based genome editing in primary human pancreatic islet cells

Gene targeting studies in primary human islets could advance our understanding of mechanisms driving diabetes pathogenesis. Here, we demonstrate successful genome editing in primary human islets using clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9). CRISPR-based targeting efficiently mutated protein-coding exons, resulting in acute loss of islet β-cell regulators, like the transcription factor PDX1 and the KATP channel subunit KIR6.2, accompanied by impaired β-cell regulation and function. CRISPR targeting of non-coding DNA harboring type 2 diabetes (T2D) risk variants revealed changes in ABCC8, SIX2 and SIX3 expression, and impaired β-cell function, thereby linking regulatory elements in these target genes to T2D genetic susceptibility. Advances here establish a paradigm for genetic studies in human islet cells, and reveal regulatory and genetic mechanisms linking non-coding variants to human diabetes risk.

The impact of non-additive genetic associations on age-related complex diseases

Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases.

Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10-72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10-4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10-5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

A Mendelian Randomization Study of Plasma Homocysteine Levels and Cerebrovascular and Neurodegenerative Diseases

Background: Homocysteine (Hcy) is a toxic amino acid and hyperhomocysteinemia (HHcy) was reported to be associated with both cerebrovascular disease and neurodegenerative disease. Our aim was to assess the causal link between plasma Hcy level and cerebrovascular and neurodegenerative diseases through a Mendelian randomization (MR) study.

Methods: A two-sample MR study was performed to infer the causal link. We extracted the genetic variants (SNPs) associated with plasma Hcy level from a large genome-wide association study (GWAS) meta-analysis. The main MR analysis was performed using the inverse variance-weighted method. Additional analyses were further performed using MR-Egger intercept and Cochran’s Q statistic to detect the heterogeneity or pleiotropy of our findings.

Results: Thirteen Hcy-associated SNPs were selected as instrumental variables. The results showed evidence of a causal link between plasma Hcy level and ischemic stroke (IS) caused by small artery occlusion (SAS, OR = 1.329, 95% CI 1.047–1.612, p = 0.048). Meanwhile, there was no evidence of association between plasma Hcy level and other types of IS, transient ischemic attack (TIA), or neurodegenerative disease. The MR-Egger intercept test indicated no evidence of directional pleiotropy. Results of additional MR analysis indicated that blood pressure (BP) and type 2 diabetes mellitus (T2DM) serve as influencers in the association.

Conclusion: The MR study found a little causal link between plasma Hcy level and SAS. The link is likely to be influenced by other risk factors like BP and T2DM.

Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.

Chromatin 3D interaction analysis of the STARD10 locus unveils FCHSD2 as a regulator of insulin secretion

Using chromatin conformation capture, we show that an enhancer cluster in the STARD10 type 2 diabetes (T2D) locus forms a defined 3-dimensional (3D) chromatin domain. A 4.1-kb region within this locus, carrying 5 T2D-associated variants, physically interacts with CTCF-binding regions and with an enhancer possessing strong transcriptional activity. Analysis of human islet 3D chromatin interaction maps identifies the FCHSD2 gene as an additional target of the enhancer cluster. CRISPR-Cas9-mediated deletion of the variant region, or of the associated enhancer, from human pancreas-derived EndoC-βH1 cells impairs glucose-stimulated insulin secretion. Expression of both STARD10 and FCHSD2 is reduced in cells harboring CRISPR deletions, and lower expression of STARD10 and FCHSD2 is associated, the latter nominally, with the possession of risk variant alleles in human islets. Finally, CRISPR-Cas9-mediated loss of STARD10 or FCHSD2, but not ARAP1, impairs regulated insulin secretion. Thus, multiple genes at the STARD10 locus influence β cell function.

A prospective study of the associations among fine particulate matter, genetic variants, and the risk of colorectal cancer

BACKGROUND

Fine particulate matter (PM_2.5) is suspected to increase the risk of colorectal cancer, but the mechanism remains unknown. We aimed to investigate the association between PM_2.5 exposure, genetic variants and colorectal cancer risk in the Prostate, Lung, Colon and Ovarian (PLCO) Cancer Screening trial.

METHODS

We included a prospective cohort of 139,534 cancer-free individuals from 10 United States research centers with over ten years of follow-up. We used a Cox regression model to assess the association between PM_2.5 exposure and colorectal cancer incidence by calculating the hazard ratio (HR) and 95% confidence interval (CI) with adjustment for potential confounders. The polygenic risk score (PRS) and genome-wide interaction analysis (GWIA) were used to evaluate the multiplicative interaction between PM_2.5 exposure and genetic variants in regard to colorectal cancer risk.

RESULTS

After a median of 10.43 years of follow-up, 1,666 participants had been diagnosed with colorectal cancer. PM_2.5 exposure was significantly associated with an increased risk of colorectal cancer (HR = 1.27; 95% CI = 1.17-1.37 per 5 μg/m³ increase). Five independent susceptibility loci reached statistical significance at P < 1.22 × 10^-8 in the interaction analysis. Furthermore, a joint interaction was observed between PM_2.5 exposure and the PRS based on these five loci with colorectal cancer risk (P = 3.11 × 10^-29). The Gene Ontology analysis showed that the vascular endothelial growth factor (VEGF) receptor signaling pathway was involved in the biological process of colorectal cancer.

CONCLUSIONS

Our large-scale analysis has shown for the first time that long-term PM_2.5 exposure potential increases colorectal cancer risk, which might be modified by genetic variants.

Human Pluripotent Stem Cells to Model Islet Defects in Diabetes

Diabetes mellitus is characterized by elevated levels of blood glucose and is ultimately caused by insufficient insulin production from pancreatic beta cells. Different research models have been utilized to unravel the molecular mechanisms leading to the onset of diabetes. The generation of pancreatic endocrine cells from human pluripotent stem cells constitutes an approach to study genetic defects leading to impaired beta cell development and function. Here, we review the recent progress in generating and characterizing functional stem cell-derived beta cells. We summarize the diabetes disease modeling possibilities that stem cells offer and the challenges that lie ahead to further improve these models.

Analysis of Glucocorticoid-Related Genes Reveal CCHCR1 as a New Candidate Gene for Type 2 Diabetes

Glucocorticoids have multiple therapeutic benefits and are used both for immunosuppression and treatment purposes. Notwithstanding their benefits, glucocorticoid use often leads to hyperglycemia. Owing to the pathophysiologic overlap in glucocorticoid-induced hyperglycemia (GIH) and type 2 diabetes (T2D), we hypothesized that genetic variation in glucocorticoid pathways contributes to T2D risk. To determine the genetic contribution of glucocorticoid action on T2D risk, we conducted multiple genetic studies. First, we performed gene-set enrichment analyses on 3 collated glucocorticoid-related gene sets using publicly available genome-wide association and whole-exome data and demonstrated that genetic variants in glucocorticoid-related genes are associated with T2D and related glycemic traits. To identify which genes are driving this association, we performed gene burden tests using whole-exome sequence data. We identified 20 genes within the glucocorticoid-related gene sets that are nominally enriched for T2D-associated protein-coding variants. The most significant association was found in coding variants in coiled-coil α-helical rod protein 1 (CCHCR1) in the HLA region (P = .001). Further analyses revealed that noncoding variants near CCHCR1 are also associated with T2D at genome-wide significance (P = 7.70 × 10-14), independent of type 1 diabetes HLA risk. Finally, gene expression and colocalization analyses demonstrate that variants associated with increased T2D risk are also associated with decreased expression of CCHCR1 in multiple tissues, implicating this gene as a potential effector transcript at this locus. Our discovery of a genetic link between glucocorticoids and T2D findings support the hypothesis that T2D and GIH may have shared underlying mechanisms.

Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis

We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ethnic meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program, DIAMANTE, Biobank Japan, and other studies. We report 568 associations, including 286 autosomal, 7 X chromosomal, and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D-associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. A polygenic risk score was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral artery disease (PAD), and neuropathy. We investigated the genetic etiology of T2D-related vascular outcomes in MVP and observed statistical SNP-T2D interactions at 13 variants, including coronary heart disease, CKD, PAD, and neuropathy. These findings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes.

Polymorphic Inversions Underlie the Shared Genetic Susceptibility of Obesity-Related Diseases

The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed an inversion association analysis including 21 inversions and 25 obesity-related traits on a total of 408,898 Europeans and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2, and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes for obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases.

Classification of Type 2 Diabetes Genetic Variants and a Novel Genetic Risk Score Association With Insulin Clearance

CONTEXT

Genome-wide association studies have identified more than 450 single nucleotide polymorphisms (SNPs) for type 2 diabetes (T2D).

OBJECTIVE

To facilitate use of these SNPs in future genetic risk score (GRS)-based analyses, we aimed to classify the SNPs based on physiology. We also sought to validate GRS associations with insulin-related traits in deeply phenotyped Mexican Americans.

DESIGN, SETTING, AND PARTICIPANTS

A total of 457 T2D SNPs from the literature were assigned physiologic function based on association studies and cluster analyses. All SNPs (All-GRS), beta-cell (BC-GRS), insulin resistance (IR-GRS), lipodystrophy (Lipo-GRS), and body mass index plus lipids (B + L-GRS) were evaluated for association with diabetes and indices of insulin secretion (from oral glucose tolerance test), insulin sensitivity and insulin clearance (from euglycemic clamp), and adiposity and lipid markers in 1587 Mexican Americans.

RESULTS

Of the 457 SNPs, 52 were classified as BC, 30 as IR, 12 as Lipo, 12 as B + L, whereas physiologic function of 351 was undefined. All-GRS was strongly associated with T2D. Among nondiabetic Mexican Americans, BC-GRS was associated with reduced insulinogenic index, IR-GRS was associated with reduced insulin sensitivity, and Lipo-GRS was associated with reduced adiposity. B + L-GRS was associated with increased insulin clearance. The latter did not replicate in an independent cohort wherein insulin clearance was assessed by a different method.

CONCLUSIONS

Supporting their utility, BC-GRS, IR-GRS, and Lipo-GRS, based on SNPs discovered largely in Europeans, exhibited expected associations in Mexican Americans. The novel association of B + L-GRS with insulin clearance suggests that impaired ability to reduce insulin clearance in compensation for IR may play a role in the pathogenesis of T2D. Whether this applies to other ethnic groups remains to be determined.

Natural Products Derived from the Mediterranean Diet with Antidiabetic Activity: from Insulin Mimetic Hypoglycemic to Nutriepigenetic Modulator Compounds

BACKGROUND

The Mediterranean diet is a healthy eating pattern that protects against the development of Type 2 diabetes mellitus (T2DM), a metabolic disease characterized by elevated blood sugar levels due to pancreatic beta-cell functional impairment and insulin resistance in various tissues. Inspired by the ancient communities, this diet emphasizes eating primarily plant-based foods, including vegetables, legumes, fruits, cereals, and nuts. Importantly, virgin olive oil is used as the principal source of fat. Red meat is consumed in low amounts while wine and fish are consumed moderately.

OBJECTIVE

Here, we review the most beneficial components of the Mediterranean Diet and tentative mechanisms of action for prevention and/or management of T2DM, based on research conducted within the last decade.

METHODS

The references over the last five years have been reviewed and they have been selected properly according to inclusion/ exclusion criteria.

RESULTS

Several bioactive diet components were evaluated to prevent inflammation and cytokine-induced oxidative damage, reduce glucose concentration, carbohydrate absorption and increase insulin sensitivity and related gene expression.

CONCLUSION

The adherence to a healthy lifestyle, including diet, exercise and habits remains the best approach for the prevention of diabetes as well as frequent check-ups and education. Though diabetes has a strong genetic component, in recent years many reports strongly point to the critical role of lifestyle specific epigenetic modifications in the development of T2DM. It remains to be established how different components of the Mediterranean Diet interact and influence the epigenetic landscape to prevent or treat the disease.

Genome-Wide Association Study on the Early-Phase Insulin Response to a Liquid Mixed Meal: Results From the NEO Study

Early-phase insulin secretion is a determinant of postprandial glucose homeostasis. In this study, we aimed to identify novel genetic variants associated with the early-phase insulin response to a liquid mixed meal by a genome-wide association study using a discovery and replication design embedded in the Netherlands Epidemiology of Obesity (NEO) study. The early-phase insulin response was defined as the difference between the natural logarithm-transformed insulin concentrations of the postprandial state at 30 min after a meal challenge and the fasting state (Δinsulin). After Bonferroni correction, rs505922 (β: -6.5% [minor allele frequency (MAF) 0.32, P = 3.3 × 10^-8]) located in the ABO gene reached genome-wide significant level (P < 5 × 10^-8) and was also replicated successfully (β: -7.8% [MAF 0.32, P = 7.2 × 10^-5]). The function of the ABO gene was assessed using in vitro shRNA-mediated knockdown of gene expression in the murine pancreatic β-cell line MIN6. Knocking down the ABO gene led to decreased insulin secretion in the murine pancreatic β-cell line. These data indicate that the previously identified elevated risk of type 2 diabetes for carriers of the ABO rs505922:C allele may be caused by decreased early-phase insulin secretion.

Pancreatic Islet Transcriptional Enhancers and Diabetes

PURPOSE OF REVIEW

Common genetic variants that associate with type 2 diabetes risk are markedly enriched in pancreatic islet transcriptional enhancers. This review discusses current advances in the annotation of islet enhancer variants and their target genes.

RECENT FINDINGS

Recent methodological advances now allow genetic and functional mapping of diabetes causal variants at unprecedented resolution. Mapping of enhancer-promoter interactions in human islets has provided a unique appreciation of the complexity of islet gene regulatory processes and enabled direct association of noncoding diabetes risk variants to their target genes. The recently improved human islet enhancer annotations constitute a framework for the interpretation of diabetes genetic signals in the context of pancreatic islet gene regulation. In the future, integration of existing and yet to come regulatory maps with genetic fine-mapping efforts and in-depth functional characterization will foster the discovery of novel diabetes molecular risk mechanisms.