Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

Impact of high glucose levels and glucose lowering on risk of ischaemic stroke: a Mendelian randomisation study and meta-analysis

AIMS/HYPOTHESIS

It is unclear whether glucose per se has a causal impact on risk of stroke and whether glucose-lowering drugs reduce this risk. This is important for the choice of treatment for individuals at risk. We tested the hypotheses that high plasma glucose has a causal impact on increased risk of ischaemic stroke, and that glucose-lowering drugs reduce this risk.

METHODS

Using a Mendelian randomisation design, we examined 118,838 individuals from two Copenhagen cohorts, the Copenhagen General Population Study and the Copenhagen City Heart Study, and 440,328 individuals from the MEGASTROKE study. Effects of eight glucose-lowering drugs on risk of stroke were summarised by meta-analyses.

RESULTS

In genetic, causal analyses, a 1 mmol/l higher plasma glucose had a risk ratio of 1.48 (95% CI 1.04, 2.11) for ischaemic stroke in the Copenhagen studies. The corresponding risk ratio from the MEGASTROKE study combined with the Copenhagen studies was 1.74 (1.31, 2.18). In meta-analyses of glucose-lowering drugs, the risk ratio for stroke was 0.85 (0.77, 0.94) for glucagon-like peptide-1 receptor agonists and 0.82 (0.69, 0.98) for thiazolidinediones, while sulfonylureas, dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter 2 inhibitors, α-glucosidase inhibitors, meglitinides and metformin individually lacked statistical evidence of an effect on stroke risk.

CONCLUSIONS/INTERPRETATION

Genetically high plasma glucose has a causal impact on increased risk of ischaemic stroke. Treatment with glucose-lowering glucagon-like peptide-1 receptor agonists and thiazolidinediones reduces this risk. These results may guide clinicians in the treatment of individuals at high risk of ischaemic stroke.

Type 2 Diabetes, Metabolic Traits, and Risk of Heart Failure: A Mendelian Randomization Study

OBJECTIVE

The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes (T2D), glycemic traits, and risk of heart failure (HF).

RESEARCH DESIGN AND METHODS

Summary-level data were obtained from genome-wide association studies of T2D, insulin resistance (IR), glycated hemoglobin, fasting insulin and glucose, and HF. MR was conducted using the inverse-variance weighted method. Sensitivity analyses included the MR-Egger method, weighted median and mode methods, and multivariable MR conditioning on potential mediators.

RESULTS

Genetic liability to T2D was causally related to higher risk of HF (odds ratio [OR] 1.13 per 1-log unit higher risk of T2D; 95% CI 1.11-1.14; P < 0.001); however, sensitivity analysis revealed evidence of directional pleiotropy. The relationship between T2D and HF was attenuated when adjusted for coronary disease, BMI, LDL cholesterol, and blood pressure in multivariable MR. Genetically instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1-log unit higher risk of IR; 95% CI 1.00-1.41; P = 0.041). There were no notable associations identified between fasting insulin, glucose, or glycated hemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of T2D (OR 1.49; 95% CI 1.01-2.19; P = 0.042), although again with evidence of pleiotropy.

CONCLUSIONS

These findings suggest a possible causal role of T2D and IR in HF etiology, although the presence of both bidirectional effects and directional pleiotropy highlights potential sources of bias that must be considered.

Dietary Acid Load and Its Interaction with IGF1 (rs35767 and rs7136446) and IL6 (rs1800796) Polymorphisms on Metabolic Traits among Postmenopausal Women

The objective of this study was to explore the effects of dietary acid load (DAL) and IGF1 and IL6 gene polymorphisms and their potential diet–gene interactions on metabolic traits. A total of 211 community-dwelling postmenopausal women were recruited. DAL was estimated using potential renal acid load (PRAL). Blood was drawn for biochemical parameters and DNA was extracted and Agena^® MassARRAY was used for genotyping analysis to identify the signalling of IGF1 (rs35767 and rs7136446) and IL6 (rs1800796) polymorphisms. Interactions between diet and genetic polymorphisms were assessed using regression analysis. The result showed that DAL was positively associated with fasting blood glucose (FBG) (β = 0.147, p < 0.05) and there was significant interaction effect between DAL and IL6 with systolic blood pressure (SBP) (β = 0.19, p = 0.041). In conclusion, these findings did not support the interaction effects between DAL and IGF1 and IL6 single nucleotide polymorphisms (rs35767, rs7136446, and rs1800796) on metabolic traits, except for SBP. Besides, higher DAL was associated with higher FBG, allowing us to postulate that high DAL is a potential risk factor for diabetes.

Insulin resistance genetic risk score and burden of coronary artery disease in patients referred for coronary angiography

AIMS

Insulin resistance associates with development of metabolic syndrome and risk of cardiovascular disease. The link between insulin resistance and cardiovascular disease is complex and multifactorial. Confirming the genetic link between insulin resistance, type 2 diabetes, and coronary artery disease, as well as the extent of coronary artery disease, is important and may provide better risk stratification for patients at risk. We investigated whether a genetic risk score of 53 single nucleotide polymorphisms known to be associated with insulin resistance phenotypes was associated with diabetes and burden of coronary artery disease.

METHODS AND RESULTS

We genotyped patients with a coronary angiography performed in the capital region of Denmark from 2010-2014 and constructed a genetic risk score of the 53 single nucleotide polymorphisms. Logistic regression using quartiles of the genetic risk score was performed to determine associations with diabetes and coronary artery disease. Associations with the extent of coronary artery disease, defined as one-, two- or three-vessel coronary artery disease, was determined by multinomial logistic regression. We identified 4,963 patients, of which 17% had diabetes and 55% had significant coronary artery disease. Of the latter, 27%, 14% and 14% had one, two or three-vessel coronary artery disease, respectively. No significant increased risk of diabetes was identified comparing the highest genetic risk score quartile with the lowest. An increased risk of coronary artery disease was found for patients with the highest genetic risk score quartile in both unadjusted and adjusted analyses, OR 1.21 (95% CI: 1.03, 1.42, p = 0.02) and 1.25 (95% CI 1.06, 1.48, p<0.01), respectively. In the adjusted multinomial logistic regression, patients in the highest genetic risk score quartile were more likely to develop three-vessel coronary artery disease compared with patients in the lowest genetic risk score quartile, OR 1.41 (95% CI: 1.10, 1.82, p<0.01).

CONCLUSIONS

Among patients referred for coronary angiography, only a strong genetic predisposition to insulin resistance was associated with risk of coronary artery disease and with a greater disease burden.

Genetic Predisposition to Type 2 Diabetes and Insulin Levels Is Positively Associated With Serum Urate Levels

PURPOSE

Previous epidemiological evidence showed that type 2 diabetes (T2D) is related with gout. However, the causality and the direction of this association are still not definitely elucidated. We investigated bidirectional associations of T2D and glycemic traits with serum urate concentrations and gout using a Mendelian randomization approach.

METHODS

Summary statistics from the large-scale genomewide association studies conducted for T2D (Ncase = 62 892, Ncontrol = 596 424), fasting glucose (N = 133 010), fasting insulin (N = 133 010), hemoglobin A1c (N = 123 665), homeostasis model assessment of insulin resistance (N = 46 186), urate (N = 110 347), and gout (Ncase = 2115, Ncontrol = 67 259) among participants of European ancestry were analyzed. For each trait of interest, independent genomewide significant (P < 5 × 10-8) single nucleotide polymorphisms were selected as instrumental variables. The inverse-variance weighted method was used for the primary analyses.

RESULTS

Genetic predisposition to higher risk of T2D [beta = 0.042; 95% confidence interval (CI) = 0.016-0.068; P = 0.002] and higher levels of fasting insulin (beta = 0.756; 95% CI = 0.408-1.102; P = 1.96e-05) were significantly associated with increased serum urate concentrations. Moreover, we found suggestively significant evidence supporting a causal role of fasting insulin on risk of developing gout (odds ratio = 3.06; 95% CI = 0.88-10.61; P = 0.078). In the reverse direction analysis, genetic predisposition to both urate and gout were not associated with T2D or any of 4 glycemic traits being investigated.

CONCLUSIONS

This study provides supportive evidence on causal associations of T2D and fasting insulin with serum urate concentrations and a suggestive association of fasting insulin with risk of gout. Future research is required to examine the underlying biological mechanisms on such relationships.

Framingham Heart Study: JACC Focus Seminar, 1/8

The Framingham Heart Study is the longest-running cardiovascular epidemiological study, starting in 1948. This paper gives an overview of the various cohorts, collected data, and most important research findings to date. In brief, the Framingham Heart Study, funded by the National Institutes of Health and managed by Boston University, spans 3 generations of well phenotyped White persons and 2 cohorts comprised of racial and ethnic minority groups. These cohorts are densely phenotyped, with extensive longitudinal follow-up, and they continue to provide us with important information on human cardiovascular and noncardiovascular physiology over the lifespan, as well as to identify major risk factors for cardiovascular disease. This paper also summarizes some of the more recent progress in molecular epidemiology and discusses the future of the study.

Fasting glucose, bone area and bone mineral density: a Mendelian randomisation study

AIMS/HYPOTHESIS

Observational studies indicate that type 2 diabetes mellitus and fasting glucose levels are associated with a greater risk for hip fracture, smaller bone area and higher bone mineral density (BMD). However, these findings may be biased by residual confounding and reverse causation. Mendelian randomisation (MR) utilises genetic variants as instruments for exposures in an attempt to address these biases. Thus, we implemented MR to determine whether fasting glucose levels in individuals without diabetes are causally associated with bone area and BMD at the total hip.

METHODS

We selected 35 SNPs strongly associated with fasting glucose (p < 5 × 10-8) in a non-diabetic European-descent population from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) (n = 133,010). MR was used to assess the associations of genetically predicted fasting glucose concentrations with total hip bone area and BMD in 4966 men and women without diabetes from the Swedish Mammography Cohort, Prospective Investigation of Vasculature in Uppsala Seniors and Uppsala Longitudinal Study of Adult Men.

RESULTS

In a meta-analysis of the three cohorts, a genetically predicted 1 mmol/l increment of fasting glucose was associated with a 2% smaller total hip bone area (-0.67 cm2 [95% CI -1.30, -0.03; p = 0.039]), yet was also associated, albeit without reaching statistical significance, with a 4% higher total hip BMD (0.040 g/cm2 [95% CI -0.00, 0.07; p = 0.060]).

CONCLUSIONS/INTERPRETATION

Fasting glucose may be a causal risk factor for smaller bone area at the hip, yet possibly for greater BMD. Further MR studies with larger sample sizes are required to corroborate these findings.

Increasing fasting glucose and fasting insulin associated with elevated bone mineral density-evidence from cross-sectional and MR studies

We performed a cross-sectional study using the National Health Examination and Nutrition Survey (NHANES) data and a Mendelian randomisation (MR) study using the GWAS summary statistics from European populations. The T2D-related indices (fasting plasma glucose (FPG), fasting insulin (FI), and insulin resistance (IR)) were found to associate with elevated bone mineral density (BMD).

INTRODUCTION

The known associations amongst FPG, FI, IR, and BMD remain inconsistent. This study aims to explore the abovementioned associations by using cross-sectional and MR designs.

METHODS

Data from adults aged ≥ 20 years (n = 7170) in four rounds of the U.S. NHANES (2005-2010 and 2013-2014) were analysed in this cross-sectional study. Multiple linear and logistic regression models were used for statistical analyses. A two-sample MR study was performed using the genome-wide association study summary statistics obtained from the Meta-analyses of Glucose and Insulin-related traits Consortium (n = 108,557) and Genetic Factors for Osteoporosis Consortium (n = 32,735) to examine the causality of the FI-BMD association.

RESULTS

Multiple linear regression revealed that FPG was positively associated with the BMDs at the hip, femur neck, and 1st lumbar spine (L1). Multiple logistic regressions revealed that FPG levels were associated with elevated BMDs at the hip and L1, and FI and IR levels were associated with elevated BMD at the hip. Patients with type 2 diabetes had higher hip BMD than those without diabetes. In the MR study, the lumbar spine BMD increased by 0.49 g/cm² (95% confidence interval: 0.01, 0.97) in response to per unit increase in log-transformed FI.

CONCLUSION

Findings from our cross-sectional and MR studies revealed the associations between the studied diabetic indices and BMD measurements in the US and European adults.

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study

BACKGROUND

The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions.

OBJECTIVE

This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone.

METHODS

A cohort of 393 participants enrolled in Digbi Health's personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data.

RESULTS

Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

CONCLUSIONS

Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants' genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficacy.

GPR119 agonists: Novel therapeutic agents for type 2 diabetes mellitus

Diabetes mellitus type 2 (T2D) is a group of genetically heterogeneous metabolic disorders whose frequency has gradually risen worldwide. Diabetes mellitus Type 2 (T2D) has started to achieve a pandemic level, and it is estimated that within the next decade, cases of diabetes might get double due to increase in aging population. Diabetes is rightly called the 'silent killer' because it has emerged to be one of the major causes, leading to renal failure, loss of vision; besides cardiac arrest in India. Thus, a clinical requirement for the oral drug molecules monitoring glucose homeostasis appears to be unmet. GPR119 agonist, a family of G-protein coupled receptors, usually noticed in β-cells of pancreatic as well as intestinal L cells, drew considerable interest for type 2 diabetes mellitus (T2D). GPR119 monitors physiological mechanisms that enhance homeostasis of glucose, such as glucose-like peptide-1, gastrointestinal incretin hormone levels, pancreatic beta cell-dependent insulin secretion and glucose-dependent insulinotropic peptide (GIP). In this manuscript, we have reviewed the work done in the last five years (2015-2020) which gives an approach to design, synthesize, evaluate and study the structural activity relationship of novel GPR119 agonist-based lead compounds. Our article would help the researchers and guide their endeavours in the direction of strategy and development of innovative, effective GPR119 agonist-based compounds for the management of diabetes mellitus type 2.

Distinct whole-blood transcriptome profile of children with metabolic healthy overweight/obesity compared to metabolic unhealthy overweight/obesity

BACKGROUND

Youth populations with overweight/obesity (OW/OB) exhibit heterogeneity in cardiometabolic health phenotypes. The underlying mechanisms for those differences are still unclear. This study aimed to analyze the whole-blood transcriptome profile (RNA-seq) of children with metabolic healthy overweight/obesity (MHO) and metabolic unhealthy overweight/obesity (MUO) phenotypes.

METHODS

Twenty-seven children with OW/OB (10.1 ± 1.3 years, 59% boys) from the ActiveBrains project were included. MHO was defined as having none of the following criteria for metabolic syndrome: elevated fasting glucose, high serum triglycerides, low high-density lipoprotein-cholesterol, and high systolic or diastolic blood pressure, while MUO was defined as presenting one or more of these criteria. Inflammatory markers were additionally determined. Total blood RNA was analyzed by 5'-end RNA-sequencing.

RESULTS

Whole-blood transcriptome analysis revealed a distinct pattern of gene expression in children with MHO compared to MUO children. Thirty-two genes differentially expressed were linked to metabolism, mitochondrial, and immune functions.

CONCLUSIONS

The identified gene expression patterns related to metabolism, mitochondrial, and immune functions contribute to a better understanding of why a subset of the population remains metabolically healthy despite having overweight/obesity.

IMPACT

A distinct pattern of whole-blood transcriptome profile (RNA-seq) was identified in children with metabolic healthy overweight/obesity (MHO) compared to metabolic unhealthy overweight/obesity (MUO) phenotype. The most relevant genes in understanding the molecular basis underlying the MHO/MUO phenotypes in children could be: RREB1, FAM83E, SLC44A1, NRG1, TMC5, CYP3A5, TRIM11, and ADAMTSL2. The identified whole-blood transcriptome profile related to metabolism, mitochondrial, and immune functions contribute to a better understanding of why a subset of the population remains metabolically healthy despite having overweight/obesity.

Dysmetabolic adipose tissue in obesity: morphological and functional characteristics of adipose stem cells and mature adipocytes in healthy and unhealthy obese subjects

The way by which subcutaneous adipose tissue (SAT) expands and undergoes remodeling by storing excess lipids through expansion of adipocytes (hypertrophy) or recruitment of new precursor cells (hyperplasia) impacts the risk of developing cardiometabolic and respiratory diseases. In unhealthy obese subjects, insulin resistance, type 2 diabetes, hypertension, and obstructive sleep apnoea are typically associated with pathologic SAT remodeling characterized by adipocyte hypertrophy, as well as chronic inflammation, hypoxia, increased visceral adipose tissue (VAT), and fatty liver. In contrast, metabolically healthy obese individuals are generally associated with SAT development characterized by the presence of smaller and numerous mature adipocytes, and a lower degree of VAT inflammation and ectopic fat accumulation. The remodeling of SAT and VAT is under genetic regulation and influenced by inherent depot-specific differences of adipose tissue-derived stem cells (ASCs). ASCs have multiple functions such as cell renewal, adipogenic capacity, and angiogenic properties, and secrete a variety of bioactive molecules involved in vascular and extracellular matrix remodeling. Understanding the mechanisms regulating the proliferative and adipogenic capacity of ASCs from SAT and VAT in response to excess calorie intake has become a focus of interest over recent decades. Here, we summarize current knowledge about the biological mechanisms able to foster or impair the recruitment and adipogenic differentiation of ASCs during SAT and VAT development, which regulate body fat distribution and favorable or unfavorable metabolic responses.

Educational attainment protects against type 2 diabetes independently of cognitive performance: a Mendelian randomization study

AIMS

Observational studies have reported a negative association between educational attainment and type 2 diabetes (T2D), but the causality remains largely unknown. The aim of this study is to investigate the potential causal effect of educational attainment on T2D and whether such an effect is independent of cognitive performance.

METHODS

We conducted two-sample Mendelian randomization (MR) analysis using genetic variants strongly associated with educational attainment and cognitive performance to estimate the causal associations with T2D, among 61,714 T2D cases and 593,952 controls. We also performed multivariable MR to explore the independent effects of educational attainment and cognitive performance on T2D risk.

RESULTS

In univariable MR, we found evidence that genetically predicted higher educational attainment [odds ratio (OR) 0.53 per 1-standard deviation (SD) increase; 95% confidence interval (CI) 0.47-0.60] and cognitive performance (OR 0.79 per 1-SD increase; 95%CI 0.69-0.91) were related to decreased risk of T2D. Our further multivariable MR results suggested that more years of education led to a reduced likelihood of T2D independently of cognitive performance (OR 0.52; 95%CI 0.42-0.64). However, the protective effect of cognitive performance on T2D was attenuated once educational attainment was controlled for (OR 1.08; 95%CI 0.88-1.32).

CONCLUSIONS

We provided evidence to suggest that educational attainment protects against T2D independently of cognitive performance, but does not support a negative causal association between cognitive performance and T2D independently of educational attainment. Education might represent a potential target for intervention to battle type 2 diabetes risk.

Investigation of glycaemic traits in psychiatric disorders using Mendelian randomisation revealed a causal relationship with anorexia nervosa

Data from observational studies have suggested an involvement of abnormal glycaemic regulation in the pathophysiology of psychiatric illness. This may be an attractive target for clinical intervention as glycaemia can be modulated by both lifestyle factors and pharmacological agents. However, observational studies are inherently confounded, and therefore, causal relationships cannot be reliably established. We employed genetic variants rigorously associated with three glycaemic traits (fasting glucose, fasting insulin, and glycated haemoglobin) as instrumental variables in a two-sample Mendelian randomisation analysis to investigate the causal effect of these measures on the risk for eight psychiatric disorders. A significant protective effect of a natural log transformed pmol/L increase in fasting insulin levels was observed for anorexia nervosa after the application of multiple testing correction (OR = 0.48 [95% CI: 0.33-0.71]-inverse-variance weighted estimate). There was no consistently strong evidence for a causal effect of glycaemic factors on the other seven psychiatric disorders considered. The relationship between fasting insulin and anorexia nervosa was supported by a suite of sensitivity analyses, with no statistical evidence of instrument heterogeneity or horizontal pleiotropy. Further investigation is required to explore the relationship between insulin levels and anorexia.

Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank

AIMS

Type 2 diabetes (T2D) is affected by a combination of genetic and environmental factors. However, the comprehensive genomic risk scores (GRSs) for T2D prediction have not been evaluated.

METHODS

Using a meta-scoring approach, we developed a metaGRS for T2D; T2D-related traits consist of 1,692 genetic variants in the UK Biobank training set (n = 40,423 + 7,558 events) and evaluate this score in the validation set (n = 303,053).

RESULTS

The hazard ratio (HR) for T2D was 1.32 (95% confidence interval [CI]: 1.29-1.35) per standard deviation of metaGRS and was larger than previously published T2D-GRS. Individuals, in the top 25% of metaGRS, have an HR of 2.08 (95%CI: 1.93-2.23) compared with those in the bottom 25%. The addition of metaGRS to all conventional risk factors significantly increased the AUC (P < 0.001). Adding metaGRS to all conventional risk factors significantly improved the reclassification accuracy (continuous net reclassification improvement = 11.8%, 95%CI: 9.2%-14.2%). All analyses adjusted for age, sex, and 10PCs.

CONCLUSIONS

The metaGRS significantly improves T2D prediction ability.

Interaction of diabetes genetic risk and successful lifestyle modification in the Diabetes Prevention Programme

AIM

To test whether diabetes genetic risk modifies the association of successful lifestyle changes with incident diabetes.

MATERIALS AND METHODS

We studied 823 individuals randomized to the intensive lifestyle intervention (ILS) arm of the Diabetes Prevention Programme who were diabetes-free 1 year after enrolment. We tested additive and multiplicative interactions of a 67-variant diabetes genetic risk score (GRS) with achievement of three ILS goals at 1 year (≥7% weight loss, ≥150 min/wk of moderate leisure-time physical activity, and/or a goal for self-reported total fat intake) on the primary outcome of incident diabetes over 3 years of follow-up.

RESULTS

A lower GRS and achieving each or all three ILS goals were each associated with lower incidence of diabetes (all P < 0.05). Additive interactions were significant between the GRS and achievement of the weight loss goal (P < 0.001), physical activity goal (P = 0.02), and all three ILS goals (P < 0.001) for diabetes risk. Achievement of all three ILS goals was associated with 1.8 (95% CI 0.3, 3.4), 3.1 (95% CI 1.5, 4.7), and 3.9 (95% CI 1.6, 6.2) fewer diabetes cases/100-person-years in the first, second and third GRS tertiles (P < 0.001 for trend). Multiplicative interactions between the GRS and ILS goal achievement were significant for the diet goal (P < 0.001), but not for weight loss (P = 0.18) or physical activity (P = 0.62) goals.

CONCLUSIONS

Genetic risk may identify high-risk subgroups for whom successful lifestyle modification is associated with greater absolute reduction in the risk of incident diabetes.

Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. We observed state-specific enrichment of fasting glucose and T2D genome-wide association studies for beta cells and enrichment for other endocrine cell types. At T2D signals localized to islet-accessible chromatin, we prioritized variants with predicted regulatory function and co-accessibility with target genes. A causal T2D variant rs231361 at the KCNQ1 locus had predicted effects on a beta cell enhancer co-accessible with INS and genome editing in embryonic stem cell-derived beta cells affected INS levels. Together our findings demonstrate the power of single-cell epigenomics for interpreting complex disease genetics.

Diabetes Mellitus, Glycemic Traits, and Cerebrovascular Disease: A Mendelian Randomization Study

OBJECTIVE

We employed Mendelian randomization to explore the effects of genetic predisposition to type 2 diabetes (T2D), hyperglycemia, insulin resistance, and pancreatic β-cell dysfunction on risk of stroke subtypes and related cerebrovascular phenotypes.

METHODS

We selected instruments for genetic predisposition to T2D (74,124 cases, 824,006 controls), HbA1c levels (n = 421,923), fasting glucose levels (n = 133,010), insulin resistance (n = 108,557), and β-cell dysfunction (n = 16,378) based on published genome-wide association studies. Applying 2-sample Mendelian randomization, we examined associations with ischemic stroke (60,341 cases, 454,450 controls), intracerebral hemorrhage (1,545 cases, 1,481 controls), and ischemic stroke subtypes (large artery, cardioembolic, small vessel stroke), as well as with related phenotypes (carotid atherosclerosis, imaging markers of cerebral white matter integrity, and brain atrophy).

RESULTS

Genetic predisposition to T2D and higher HbA1c levels were associated with higher risk of any ischemic stroke, large artery stroke, and small vessel stroke. Similar associations were also noted for carotid atherosclerotic plaque, fractional anisotropy, a white matter disease marker, and markers of brain atrophy. We further found associations of genetic predisposition to insulin resistance with large artery and small vessel stroke, whereas predisposition to β-cell dysfunction was associated with small vessel stroke, intracerebral hemorrhage, lower gray matter volume, and total brain volume.

CONCLUSIONS

This study supports causal effects of T2D and hyperglycemia on large artery and small vessel stroke. We show associations of genetically predicted insulin resistance and β-cell dysfunction with large artery and small vessel stroke that might have implications for antidiabetic treatments targeting these mechanisms.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that genetic predisposition to T2D and higher HbA1c levels are associated with a higher risk of large artery and small vessel ischemic stroke.

Insulin resistance in diabetes: The promise of using induced pluripotent stem cell technology

Insulin resistance (IR) is associated with several metabolic disorders, including type 2 diabetes (T2D). The development of IR in insulin target tissues involves genetic and acquired factors. Persons at genetic risk for T2D tend to develop IR several years before glucose intolerance. Several rodent models for both IR and T2D are being used to study the disease pathogenesis; however, these models cannot recapitulate all the aspects of this complex disorder as seen in each individual. Human pluripotent stem cells (hPSCs) can overcome the hurdles faced with the classical mouse models for studying IR. Human induced pluripotent stem cells (hiPSCs) can be generated from the somatic cells of the patients without the need to destroy a human embryo. Therefore, patient-specific hiPSCs can generate cells genetically identical to IR individuals, which can help in distinguishing between genetic and acquired defects in insulin sensitivity. Combining the technologies of genome editing and hiPSCs may provide important information about the genetic factors underlying the development of different forms of IR. Further studies are required to fill the gaps in understanding the pathogenesis of IR and diabetes. In this review, we summarize the factors involved in the development of IR in the insulin-target tissues leading to diabetes. Also, we highlight the use of hPSCs to understand the mechanisms underlying the development of IR.

Type 2 Diabetes and Cognitive Status in the Health and Retirement Study: A Mendelian Randomization Approach

Background

Type 2 diabetes mellitus (T2DM) and dementia are leading causes of mortality and disability in the US. T2DM has been associated with dementia; however, causality has not been clearly established. This study tested inferred causality between T2DM and dementia status using a Mendelian randomization approach.

Methods

Participants (50+ years) from the 2010 wave of the Health and Retirement Study of European or African genetic ancestry were included (n = 10,322). History of T2DM was self-reported. Cognitive status (dementia, cognitive impairment non-dementia, or normal cognition) was defined from clinically validated cognitive assessments. Cumulative genetic risk for T2DM was determined using a polygenic score calculated from a European ancestry T2DM genome-wide association study by Xue et al. (2018). All models were adjusted for age, sex, education, APOE-ε4 carrier status, and genetic principal components. Multivariable logistic regression was used to test the association between cumulative genetic risk for T2DM and cognitive status. To test inferred causality using Mendelian randomization, we used the inverse variance method.

Results

Among included participants, 20.9% had T2DM and 20.7% had dementia or cognitive impairment. Among European ancestry participants, T2DM was associated with 1.66 times odds of cognitive impairment non-dementia (95% confidence interval: 1.55–1.77) relative to normal cognition. A one standard deviation increase in cumulative genetic risk for T2DM was associated with 1.30 times higher odds of T2DM (95% confidence interval: 1.10–1.52). Cumulative genetic risk for T2DM was not associated with dementia status or cognitive-impaired non-dementia in either ancestry (P > 0.05); lack of association here is an important assumption of Mendelian randomization. Using Mendelian randomization, we did not observe evidence for an inferred causal association between T2DM and cognitive impairment (odds ratio: 1.04; 95% confidence interval: 0.90–1.21).

Discussion

Consistent with prior research, T2DM was associated with cognitive status. Prevention of T2DM and cognitive decline are both critical for public health, however, this study does not provide evidence that T2DM is causally related to impaired cognition. Additional studies in other ancestries, larger sample sizes, and longitudinal studies are needed to confirm these results.

Evaluation of glycemic traits in susceptibility to COVID-19 risk: a Mendelian randomization study

BACKGROUND

Observational studies suggest poorer glycemic traits and type 2 diabetes associated with coronavirus disease 2019 (COVID-19) risk although these findings could be confounded by socioeconomic position. We conducted a two-sample Mendelian randomization to clarify their role in COVID-19 risk and specific COVID-19 phenotypes (hospitalized and severe cases).

METHOD

We identified genetic instruments for fasting glucose (n = 133,010), 2 h glucose (n = 42,854), glycated hemoglobin (n = 123,665), and type 2 diabetes (74,124 cases and 824,006 controls) from genome wide association studies and applied them to COVID-19 Host Genetics Initiative summary statistics (17,965 COVID-19 cases and 1,370,547 population controls). We used inverse variance weighting to obtain the causal estimates of glycemic traits and genetic predisposition to type 2 diabetes in COVID-19 risk. Sensitivity analyses included MR-Egger and weighted median method.

RESULTS

We found genetic predisposition to type 2 diabetes was not associated with any COVID-19 phenotype (OR: 1.00 per unit increase in log odds of having diabetes, 95%CI 0.97 to 1.04 for overall COVID-19; OR: 1.02, 95%CI 0.95 to 1.09 for hospitalized COVID-19; and OR: 1.00, 95%CI 0.93 to 1.08 for severe COVID-19). There were no strong evidence for an association of glycemic traits in COVID-19 phenotypes, apart from a potential inverse association for fasting glucose albeit with wide confidence interval.

CONCLUSION

We provide some genetic evidence that poorer glycemic traits and predisposition to type 2 diabetes unlikely increase the risk of COVID-19. Although our study did not indicate glycemic traits increase severity of COVID-19, additional studies are needed to verify our findings.

All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes

Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.

Genetic association and causal inference converge on hyperglycaemia as a modifiable factor to improve lung function

Measures of lung function are heritable, and thus, we sought to utilise genetics to propose drug-repurposing candidates that could improve respiratory outcomes. Lung function measures were found to be genetically correlated with seven druggable biochemical traits, with further evidence of a causal relationship between increased fasting glucose and diminished lung function. Moreover, we developed polygenic scores for lung function specifically within pathways with known drug targets and investigated their relationship with pulmonary phenotypes and gene expression in independent cohorts to prioritise individuals who may benefit from particular drug-repurposing opportunities. A transcriptome-wide association study (TWAS) of lung function was then performed which identified several drug–gene interactions with predicted lung function increasing modes of action. Drugs that regulate blood glucose were uncovered through both polygenic scoring and TWAS methodologies. In summary, we provided genetic justification for a number of novel drug-repurposing opportunities that could improve lung function.

Chronic respiratory disorders like asthma affect around 600 million people worldwide. Although these illnesses are widespread, they can have several different underlying causes, making them difficult to treat. Drugs that work well on one type of respiratory disorder may be completely ineffective on another. Understanding the biological and environmental factors that cause these illnesses will allow them to be treated more effectively by tailoring therapies to each patient.

Reduced lung function is a factor in respiratory disorders and it can have many genetic causes. Studying the genes of patients with reduced lung function can reveal the genes involved, some of which may already be targets of existing drugs for other illnesses. So, could a patient’s genetics be used to repurpose existing drugs to treat their respiratory disorders?

Reay et al. combined three methods to link genetics and biological processes to the causes of reduced lung function. The results reveal several factors that could lead to new treatments. In one example, reduced lung function showed a link to genes associated with high blood sugar. As such, treatments used in diabetes might help improve lung function in some patients. Reay et al. also developed a scoring system that could predict the efficacy of a treatment based on a patient’s genetics. The study suggests that COVID-19 infection could be affected by blood sugar levels too.

Chronic respiratory disorders are a critical issue worldwide and have proven difficult to treat, but these results suggest a way to identify new therapies and target them to the right patients. The findings also support a connection between lung function and blood sugar levels. This implies that perhaps existing diabetes treatments – including diet and lifestyle changes aimed at reducing or limiting blood sugar – could be repurposed to treat respiratory disorders in some patients. The next step will be to perform clinical trials to test whether these therapies are in fact effective.

Systematic analysis of binding of transcription factors to noncoding variants

Many sequence variants have been linked to complex human traits and diseases1, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human transcription factors to 95,886 noncoding variants in the human genome using an ultra-high-throughput multiplex protein-DNA binding assay, termed single-nucleotide polymorphism evaluation by systematic evolution of ligands by exponential enrichment (SNP-SELEX). The resulting 828 million measurements of transcription factor-DNA interactions enable estimation of the relative affinity of these transcription factors to each variant in vitro and evaluation of the current methods to predict the effects of noncoding variants on transcription factor binding. We show that the position weight matrices of most transcription factors lack sufficient predictive power, whereas the support vector machine combined with the gapped k-mer representation show much improved performance, when assessed on results from independent SNP-SELEX experiments involving a new set of 61,020 sequence variants. We report highly predictive models for 94 human transcription factors and demonstrate their utility in genome-wide association studies and understanding of the molecular pathways involved in diverse human traits and diseases.

Genetics of 35 blood and urine biomarkers in the UK Biobank

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

Cardiovascular risk factors and lifestyle behaviours in relation to longevity: a Mendelian randomization study

BACKGROUND

The American Heart Association introduced the Life's Simple 7 initiative to improve cardiovascular health by modifying cardiovascular risk factors and lifestyle behaviours. It is unclear whether these risk factors are causally associated with longevity.

OBJECTIVES

This study aimed to investigate causal associations of Life's Simple 7 modifiable risk factors, as well as sleep and education, with longevity using the two-sample Mendelian randomization design.

METHODS

Instrumental variables for the modifiable risk factors were obtained from large-scale genome-wide association studies. Data on longevity beyond the 90^th survival percentile were extracted from a genome-wide association meta-analysis with 11,262 cases and 25,483 controls whose age at death or last contact was ≤ the 60^th survival percentile.

RESULTS

Risk factors associated with a lower odds of longevity included the following: genetic liability to type 2 diabetes (OR 0.88; 95% CI: 0.84;0.92), genetically predicted systolic and diastolic blood pressure (per 1-mmHg increase: 0.96; 0.94;0.97 and 0.95; 0.93;0.97), body mass index (per 1-SD increase: 0.80; 0.74;0.86), low-density lipoprotein cholesterol (per 1-SD increase: 0.75; 0.65;0.86) and smoking initiation (0.75; 0.66;0.85). Genetically increased high-density lipoprotein cholesterol (per 1-SD increase: 1.23; 1.08;1.41) and educational level (per 1-SD increase: 1.64; 1.45;1.86) were associated with a higher odds of longevity. Fasting glucose and other lifestyle factors were not significantly associated with longevity.

CONCLUSION

Most of the Life's Simple 7 modifiable risk factors are causally related to longevity. Prevention strategies should focus on modifying these risk factors and reducing education inequalities to improve cardiovascular health and longevity.

Causal Effect of the Triglyceride-Glucose Index and the Joint Exposure of Higher Glucose and Triglyceride With Extensive Cardio-Cerebrovascular Metabolic Outcomes in the UK Biobank: A Mendelian Randomization Study

Background: The causal evidence of the triglyceride-glucose (TyG) index, as well as the joint exposure of higher glucose and triglyceride on the risk of cardio-cerebrovascular diseases (CVD), was lacking. Methods: A comprehensive factorial Mendelian randomization (MR) was performed in the UK Biobank cohort involving 273,368 individuals with European ancestry to assess and quantify these effects. The factorial MR, MR-PRESSO, MR-Egger, meta-regression, sensitivity analysis, positive control, and external verification were utilized. Outcomes include major outcomes [overall CVD, ischemic heart diseases (IHD), and cerebrovascular diseases (CED)] and minor outcomes [angina pectoris (AP), acute myocardial infarction (AMI), chronic IHD (CIHD), heart failure (HF), hemorrhagic stroke (HS), and ischemic stroke (IS)]. Results: The TyG index significantly increased the risk of overall CVD [OR (95% CI): 1.20 (1.14-1.25)], IHD [OR (95% CI): 1.22 (1.15-1.29)], CED [OR (95% CI): 1.14 (1.05-1.23)], AP [OR (95% CI): 1.29 (1.20-1.39)], AMI [OR (95% CI): 1.27 (1.16-1.39)], CIHD [OR (95% CI): 1.21 (1.13-1.29)], and IS [OR (95% CI): 1.22 (1.06-1.40)]. Joint exposure to genetically higher GLU and TG was significantly associated with a higher risk of overall CVD [OR (95% CI): 1.17 (1.12-1.23)] and IHD [OR (95% CI): 1.22 (1.16-1.29)], but not with CED. The effect of GLU and TG was independent of each other genetically and presented dose-response effects in bivariate meta-regression analysis. Conclusions: Lifelong genetic exposure to higher GLU and TG was jointly associated with higher cardiac metabolic risk while the TyG index additionally associated with several cerebrovascular diseases. The TyG index could serve as a more sensitive pre-diagnostic indicator for CVD while the joint GLU and TG could offer a quantitative risk for cardiac metabolic outcomes.

Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis

BACKGROUND

It remains unclear whether an increased risk of type 2 diabetes (T2D) affects the risk of osteoarthritis (OA).

METHODS

Here, we used two-sample Mendelian randomization (MR) to obtain non-confounded estimates of the effect of T2D and glycemic traits on hip and knee OA. We identified single-nucleotide polymorphisms (SNPs) strongly associated with T2D, fasting glucose (FG), and 2-h postprandial glucose (2hGlu) from genome-wide association studies (GWAS). We used the MR inverse variance weighted (IVW), the MR-Egger method, the weighted median (WM), and the Robust Adjusted Profile Score (MR.RAPS) to reveal the associations of T2D, FG, and 2hGlu with hip and knee OA risks. Sensitivity analyses were also conducted to verify whether heterogeneity and pleiotropy can bias the MR results.

RESULTS

We did not find statistically significant causal effects of genetically increased T2D risk, FG, and 2hGlu on hip and knee OA (e.g., T2D and hip OA, MR-Egger OR = 1.1708, 95% CI 0.9469-1.4476, p = 0.1547). It was confirmed that horizontal pleiotropy was unlikely to bias the causality (e.g., T2D and hip OA, MR-Egger, intercept = -0.0105, p = 0.1367). No evidence of heterogeneity was found between the genetic variants (e.g., T2D and hip OA, MR-Egger Q = 30.1362, I 2 < 0.0001, p = 0.6104).

CONCLUSION

Our MR study did not support causal effects of a genetically increased T2D risk, FG, and 2hGlu on hip and knee OA risk.

Multiple Linear Regression Allows Weighted Burden Analysis of Rare Coding Variants in an Ethnically Heterogeneous Population

Weighted burden analysis has been used in exome-sequenced case-control studies to identify genes in which there is an excess of rare and/or functional variants associated with phenotype. Implementation in a ridge regression framework allows simultaneous analysis of all variants along with relevant covariates, such as population principal components. In order to apply the approach to a quantitative phenotype, a weighted burden score is derived for each subject and included in a linear regression analysis. The weighting scheme is adjusted in order to apply differential weights to rare and very rare variants and a score is derived based on both the frequency and predicted effect of each variant. When applied to an ethnically heterogeneous dataset consisting of 49,790 exome-sequenced UK Biobank subjects and using body mass index as the phenotype, the method produces a very inflated test statistic. However, this is almost completely corrected by including 20 population principal components as covariates. When this is done, the top 30 genes include a few which are quite plausibly associated with the phenotype, including LYPLAL1 and NSDHL. This approach offers a way to carry out gene-based analyses of rare variants identified by exome sequencing in heterogeneous datasets without requiring that data from ethnic minority subjects be discarded. This research has been conducted using the UK Biobank Resource.

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Hypothalamus and weight loss in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating progressive neurodegenerative disorder. While initially pathophysiology was thought to be restricted to motor deficits, it is increasingly recognized that patients develop prominent changes in weight and eating behavior that result from and mediate the underlying neurodegenerative process. These changes include alterations in metabolism, lipid levels, and insulin resistance. Emerging research suggests that these alterations may be mediated through changes in the hypothalamic function, with atrophy of the hypothalamus shown in both ALS patients and also presymptomatic genetic at-risk patients. This chapter reviews the evidence for hypothalamic involvement in ALS, including melanocortin pathways and potential treatment targets.

Evidence for a causal association between milk intake and cardiometabolic disease outcomes using a two-sample Mendelian Randomization analysis in up to 1,904,220 individuals

BACKGROUND

High milk intake has been associated with cardio-metabolic risk. We conducted a Mendelian Randomization (MR) study to obtain evidence for the causal relationship between milk consumption and cardio-metabolic traits using the lactase persistence (LCT-13910 C > T, rs4988235) variant as an instrumental variable.

METHODS

We tested the association of LCT genotype with milk consumption (for validation) and with cardio-metabolic traits (for a possible causal association) in a meta-analysis of the data from three large-scale population-based studies (1958 British Birth Cohort, Health and Retirement study, and UK Biobank) with up to 417,236 participants and using summary statistics from consortia meta-analyses on intermediate traits (N = 123,665-697,307) and extended to cover disease endpoints (N = 86,995-149,821).

RESULTS

In the UK Biobank, carriers of 'T' allele of LCT variant were more likely to consume milk (P = 7.02 × 10^-14). In meta-analysis including UK Biobank, the 1958BC, the HRS, and consortia-based studies, under an additive model, 'T' allele was associated with higher body mass index (BMI) (P_{meta-analysis} = 4.68 × 10^-12) and lower total cholesterol (TC) (P = 2.40 × 10^-36), low-density lipoprotein cholesterol (LDL-C) (P = 2.08 × 10^-26) and high-density lipoprotein cholesterol (HDL-C) (P = 9.40 × 10^-13). In consortia meta-analyses, 'T' allele was associated with a lower risk of coronary artery disease (OR:0.86, 95% CI:0.75-0.99) but not with type 2 diabetes (OR:1.06, 95% CI:0.97-1.16). Furthermore, the two-sample MR analysis showed a causal association between genetically instrumented milk intake and higher BMI (P = 3.60 × 10^-5) and body fat (total body fat, leg fat, arm fat and trunk fat; P < 1.37 × 10^-6) and lower LDL-C (P = 3.60 × 10^-6), TC (P = 1.90 × 10^-6) and HDL-C (P = 3.00 × 10^-5).

CONCLUSIONS

Our large-scale MR study provides genetic evidence for the association of milk consumption with higher BMI but lower serum cholesterol levels. These data suggest no need to limit milk intakes with respect to cardiovascular disease risk, with the suggested benefits requiring confirmation in further studies.

A Polygenic Score for Type 2 Diabetes Risk Is Associated With Both the Acute and Sustained Response to Sulfonylureas

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study (P = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

Insulin resistance is characterized by a defective response (“resistance”) to normal insulin concentrations to uptake the glucose present in the blood, and is the underlying condition that leads to type 2 diabetes (T2D) and increases the risk of cardiovascular disease. It is estimated that 25–33% of the US population are insulin resistant enough to be at risk of serious clinical consequences. For more than a decade, large population studies have tried to discover the genes that participate in the development of insulin resistance, but without much success. It is now increasingly clear that the complex genetic nature of insulin resistance requires novel approaches centered in patient specific cellular models. To fill this gap, we have generated an induced pluripotent stem cell (iPSC) library from individuals with accurate measurements of insulin sensitivity, and performed gene expression and key driver analyses. Our work demonstrates that iPSCs can be used as a revolutionary technology to model insulin resistance and to discover key genetic drivers. Moreover, they can develop our basic knowledge of the disease, and are ultimately expected to increase the therapeutic targets to treat insulin resistance and type 2 diabetes.

Causal Evaluation of Laboratory Markers in Type 2 Diabetes on Cancer and Vascular Diseases Using Various Mendelian Randomization Tools

Multiple studies have demonstrated the effects of type 2 diabetes (T2D) on various human diseases; however, most of these were observational epidemiological studies that suffered from many potential biases including reported confounding and reverse causations. In this article, we investigated whether cancer and vascular disease can be affected by T2D-related traits, including fasting plasma glucose (FPG), 2-h postprandial glucose (2h-PG), and glycated hemoglobin A1c (HbA1c) levels, by using Mendelian randomization (MR). The summary statistics for FPG, 2h-PG, and HbA1c level were obtained through meta-analyses of large-scale genome-wide association studies that included data from 133,010 nondiabetic individuals from collaborating Meta-analysis of Glucose and Insulin Related Traits Consortium studies. Thereafter, based on the statistical assumptions for MR analyses, the most reliable approaches including inverse-variance-weighted (IVW), MR-Egger, MR-Egger with a simulation extrapolation (SIMEX), weighted median, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods were applied to identify traits affected by FPG, 2h-PG, and HbAlc. We found that coronary artery disease is affected by FPG, as per the IVW [log odds ratio (logOR): 0.21; P = 0.012], MR-Egger (SIMEX) (logOR: 0.22; P = 0.014), MR-PRESSO (logOR: 0.18; P = 0.045), and weighted median (logOR: 0.29; P < 0.001) methods but not as per the MR-Egger (logOR: 0.13; P = 0.426) approach. Furthermore, low-density lipoprotein cholesterol levels are affected by HbA1c, as per the IVW [beta (B): 0.23; P = 0.015), MR-Egger (B: 0.45; P = 0.046), MR-Egger (SIMEX) (B: 0.27; P = 0.007), MR-PRESSO (B; 0.14; P = 0.010), and the weighted median (B: 0.15; P = 0.012] methods. Further studies of the associated biological mechanisms are required to validate and understand the disease-specific differences identified in the TD2-related causal effects of each trait.

Mendelian Randomization Study on Amino Acid Metabolism Suggests Tyrosine as Causal Trait for Type 2 Diabetes

Circulating levels of branched-chain amino acids, glycine, or aromatic amino acids have been associated with risk of type 2 diabetes. However, whether those associations reflect causal relationships or are rather driven by early processes of disease development is unclear. We selected diabetes-related amino acid ratios based on metabolic network structures and investigated causal effects of these ratios and single amino acids on the risk of type 2 diabetes in two-sample Mendelian randomization studies. Selection of genetic instruments for amino acid traits relied on genome-wide association studies in a representative sub-cohort (up to 2265 participants) of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study and public data from genome-wide association studies on single amino acids. For the selected instruments, outcome associations were drawn from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis, 74,124 cases and 824,006 controls) consortium. Mendelian randomization results indicate an inverse association for a per standard deviation increase in ln-transformed tyrosine/methionine ratio with type 2 diabetes (OR = 0.87 (0.81-0.93)). Multivariable Mendelian randomization revealed inverse association for higher log10-transformed tyrosine levels with type 2 diabetes (OR = 0.19 (0.04-0.88)), independent of other amino acids. Tyrosine might be a causal trait for type 2 diabetes independent of other diabetes-associated amino acids.

Insights into genetic variants associated with NASH-fibrosis from metabolite profiling

Several genetic discoveries robustly implicate five single-nucleotide variants in the progression of non-alcoholic fatty liver disease to non-alcoholic steatohepatitis and fibrosis (NASH-fibrosis), including a recently identified variant in MTARC1. To better understand these variants as potential therapeutic targets, we aimed to characterize their impact on metabolism using comprehensive metabolomics data from two population-based studies. A total of 9135 participants from the Fenland study and 9902 participants from the EPIC-Norfolk cohort were included in the study. We identified individuals with risk alleles associated with NASH-fibrosis: rs738409C>G in PNPLA3, rs58542926C>T in TM6SF2, rs641738C>T near MBOAT7, rs72613567TA>T in HSD17B13 and rs2642438A>G in MTARC1. Circulating levels of 1449 metabolites were measured using targeted and untargeted metabolomics. Associations between NASH-fibrosis variants and metabolites were assessed using linear regression. The specificity of variant-metabolite associations were compared to metabolite associations with ultrasound-defined steatosis, gene variants linked to liver fat (in GCKR, PPP1R3B and LYPLAL1) and gene variants linked to cirrhosis (in HFE and SERPINA1). Each NASH-fibrosis variant demonstrated a specific metabolite profile with little overlap (8/97 metabolites) comprising diverse aspects of lipid metabolism. Risk alleles in PNPLA3 and HSD17B13 were both associated with higher 3-methylglutarylcarnitine and three variants were associated with lower lysophosphatidylcholine C14:0. The risk allele in MTARC1 was associated with higher levels of sphingomyelins. There was no overlap with metabolites that associated with HFE or SERPINA1 variants. Our results suggest a link between the NASH-protective variant in MTARC1 to the metabolism of sphingomyelins and identify distinct molecular patterns associated with each of the NASH-fibrosis variants under investigation.

Genetic architecture of host proteins involved in SARS-CoV-2 infection

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension

Hypertension is a major risk factor for cardiovascular disease and mortality. To identify targets for the prevention of hypertension and its associated disease burden, we used the 2-sample Mendelian randomization method to investigate the causal associations of 18 cardiovascular risk factors and lifestyle behaviors with hypertension. From European-descent genome-wide association studies, we selected genetic variants (P<5×10−8) for type 2 diabetes, fasting glucose, lipids, body mass index, smoking, alcohol and coffee consumption, physical activity, sleep duration, insomnia, and educational level. We extracted the genetic associations with hypertension from 2 European cohorts: the FinnGen Study (15 870 cases and 74 345 controls) and UK Biobank (54 358 cases and 408 652 controls). The inverse-variance weighted method was used as main analysis method. Genetically predicted triglycerides (pooled odds ratio [OR] per 1 SD, 1.17 [1.10–1.25]), body mass index (OR per 1 SD, 1.42 [1.37–1.48]), alcohol dependence (OR, 1.10 [1.06–1.13]), and insomnia (OR, 1.17 [1.13–1.20]) were associated with a higher odds of hypertension. Higher genetically predicted high-density lipoprotein cholesterol (OR per 1 SD, 0.88 [0.83–0.94]) and educational level (OR per 1 SD, 0.56 [0.54–0.59]) were associated with a lower odds of hypertension. Suggestive evidence was obtained for type 2 diabetes, smoking initiation and alcohol consumption with a higher hypertension odds, and longer sleep duration with a lower hypertension odds. This Mendelian randomization study identified high-density lipoprotein cholesterol, triglycerides, body mass index, alcohol dependence, insomnia, and educational level as causal risk factors for hypertension. This implicates that these modifiable risk factors are important targets in the prevention of hypertension.

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus

BACKGROUND

Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D).

METHODS

To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D).

RESULTS

None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background.

CONCLUSIONS

This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.

In Vivo Reporter Assays Uncover Changes in Enhancer Activity Caused by Type 2 Diabetes-Associated Single Nucleotide Polymorphisms

Many single nucleotide polymorphisms (SNPs) associated with type 2 diabetes overlap with putative endocrine pancreatic enhancers, suggesting that these SNPs modulate enhancer activity and, consequently, gene expression. We performed in vivo mosaic transgenesis assays in zebrafish to quantitatively test the enhancer activity of type 2 diabetes-associated loci. Six out of 10 tested sequences are endocrine pancreatic enhancers. The risk variant of two sequences decreased enhancer activity, while in another two incremented it. One of the latter (rs13266634) locates in an SLC30A8 exon, encoding a tryptophan-to-arginine substitution that decreases SLC30A8 function, which is the canonical explanation for type 2 diabetes risk association. However, other type 2 diabetes-associated SNPs that truncate SLC30A8 confer protection from this disease, contradicting this explanation. Here, we clarify this incongruence, showing that rs13266634 boosts the activity of an overlapping enhancer and suggesting an SLC30A8 gain of function as the cause for the increased risk for the disease. We further dissected the functionality of this enhancer, finding a single nucleotide mutation sufficient to impair its activity. Overall, this work assesses in vivo the importance of disease-associated SNPs in the activity of endocrine pancreatic enhancers, including a poorly explored case where a coding SNP modulates the activity of an enhancer.

The role of cortisol in ischemic heart disease, ischemic stroke, type 2 diabetes, and cardiovascular disease risk factors: a bi-directional Mendelian randomization study

BACKGROUND

Cortisol, a steroid hormone frequently used as a biomarker of stress, is associated with cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). To clarify whether cortisol causes these outcomes, we assessed the role of cortisol in ischemic heart disease (IHD), ischemic stroke, T2DM, and CVD risk factors using a bi-directional Mendelian randomization (MR) study.

METHODS

Single nucleotide polymorphisms (SNPs) strongly (P < 5 × 10-6) and independently (r2 < 0.001) predicting cortisol were obtained from the CORtisol NETwork (CORNET) consortium (n = 12,597) and two metabolomics genome-wide association studies (GWAS) (n = 7824 and n = 2049). They were applied to GWAS of the primary outcomes (IHD, ischemic stroke and T2DM) and secondary outcomes (adiposity, glycemic traits, blood pressure and lipids) to obtain estimates using inverse variance weighting, with weighted median, MR-Egger, and MR-PRESSO as sensitivity analyses. Conversely, SNPs predicting IHD, ischemic stroke, and T2DM were applied to the cortisol GWAS.

RESULTS

Genetically predicted cortisol (based on 6 SNPs from CORNET; F-statistic = 28.3) was not associated with IHD (odds ratio (OR) 0.98 per 1 unit increase in log-transformed cortisol, 95% confidence interval (CI) 0.93-1.03), ischemic stroke (0.99, 95% CI 0.91-1.08), T2DM (1.00, 95% CI 0.96-1.04), or CVD risk factors. Genetically predicted IHD, ischemic stroke, and T2DM were not associated with cortisol.

CONCLUSIONS

Contrary to observational studies, genetically predicted cortisol was unrelated to IHD, ischemic stroke, T2DM, or CVD risk factors, or vice versa. Our MR results find no evidence that cortisol plays a role in cardiovascular risk, casting doubts on the cortisol-related pathway, although replication is warranted.

Glycemic traits and Alzheimer's disease: a Mendelian randomization study

Previous observational studies have reported an association between impaired glucose metabolism and Alzheimer’s disease. This study aimed to examine the causal association of glycemic traits with Alzheimer’s disease. We used a two-sample Mendelian randomization approach to evaluate the causal effect of six glycemic traits (type 2 diabetes, fasting glucose, fasting insulin, hemoglobin A1c, homeostasis model assessment- insulin resistance and HOMA-β-cell function) on Alzheimer’s disease. Summary data on the association of single nucleotide polymorphisms with these glycemic traits were obtained from genome-wide association studies of the DIAbetes Genetics Replication And Meta-analysis and Meta-Analyses of Glucose and Insulin-related traits Consortium. Summary data on the association of single nucleotide polymorphisms with Alzheimer’s disease were obtained from the International Genomics of Alzheimer's Project. The Mendelian randomization analysis showed that 1-standard deviation higher fasting glucose and lower HOMA-β-cell function (indicating pancreatic β-cell dysfunction) were causally associated with a substantial increase in risk of Alzheimer’s disease (odds ratio=1.33, 95% confidence interval: 1.04-1.68, p=0.02; odds ratio=1.92, 95% confidence interval: 1.15-3.21, p=0.01). However, no significant association was observed for other glycemic traits. This Mendelian randomization analysis provides evidence of causal associations between glycemic traits, especially high fasting glucose and pancreatic β-cell dysfunction, and high risk of Alzheimer's disease.

Genetic Studies of Gestational Diabetes and Glucose Metabolism in Pregnancy

PURPOSE OF REVIEW

In this review, we summarize studies investigating genetics of gestational diabetes mellitus (GDM) and glucose metabolism in pregnancy. We describe these studies in the context of the larger body of literature on type 2 diabetes (T2D) and glycemic trait genomics.

RECENT FINDINGS

We reviewed 23 genetic association studies for GDM and performed a meta-analysis, which revealed variants at eight T2D loci significantly associated with GDM after the Bonferroni correction. These studies suggest that GDM and T2D share a number of genetic risk loci. Only two unbiased genome-wide association studies (GWASs) have successfully revealed genetic associations for GDM and related glycemic traits in pregnancy. A GWAS for GDM in Korean women identified two loci (near CDKAL1 and MTNR1B) known to be associated with T2D, though the association of the MTNR1B locus with GDM appears to be stronger than that for T2D. A multi-ethnic GWAS for glycemic traits in pregnancy identified two novel loci (near HKDC1 and BACE2) which appear to be associated with post-load glucose and fasting c-peptide specifically in pregnant women. There are ongoing efforts to use this genetic information, in the form of polygenic scores, to predict risk of GDM and postpartum T2D. The body of literature examining genetic associations with GDM is limited, especially when compared to the available literature on T2D and glycemic trait genomics. Additional genetic discovery for glucose metabolism in pregnant women will require larger pregnancy cohorts and international collaborative efforts. Studies on the clinical implications of these findings are also warranted.

All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes

Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.

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.

β-Cell-Specific Deletion of HMG-CoA (3-hydroxy-3-methylglutaryl-coenzyme A) Reductase Causes Overt Diabetes due to Reduction of β-Cell Mass and Impaired Insulin Secretion

Inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), statins, which are used to prevent cardiovascular diseases, are associated with a modest increase in the risk of new-onset diabetes. To investigate the role of HMGCR in the development of β-cells and glucose homeostasis, we deleted Hmgcr in a β-cell-specific manner by using the Cre-loxP technique. Mice lacking Hmgcr in β-cells (β-KO) exhibited hypoinsulinemic hyperglycemia as early as postnatal day 9 (P9) due to decreases in both β-cell mass and insulin secretion. Ki67-positive cells were reduced in β-KO mice at P9; thus, β-cell mass reduction was caused by proliferation disorder immediately after birth. The mRNA expression of neurogenin3 (Ngn3), which is transiently expressed in endocrine progenitors of the embryonic pancreas, was maintained despite a striking reduction in the expression of β-cell-associated genes, such as insulin, pancreatic and duodenal homeobox 1 (Pdx1), and MAF BZIP transcription factor A (Mafa) in the islets from β-KO mice. Histological analyses revealed dysmorphic islets with markedly reduced numbers of β-cells, some of which were also positive for glucagon. In conclusion, HMGCR plays critical roles not only in insulin secretion but also in the development of β-cells in mice.

Meta-analysis investigating the role of interleukin-6 mediated inflammation in type 2 diabetes

BACKGROUND

Evidence from animal models and observational epidemiology points to a role for chronic inflammation, in which interleukin 6 (IL-6) is a key player, in the pathophysiology of type 2 diabetes (T2D). However, it is unknown whether IL-6 mediated inflammation is implicated in the pathophysiology of T2D.

METHODS

We performed a meta-analysis of 15 prospective studies to investigate associations between IL-6 levels and incident T2D including 5,421 cases and 31,562 non-cases. We also estimated the association of a loss-of-function missense variant (Asp358Ala) in the IL-6 receptor gene (IL6R), previously shown to mimic the effects of IL-6R inhibition, in a large trans-ethnic meta-analysis of six T2D case-control studies including 260,614 cases and 1,350,640 controls.

FINDINGS

In a meta-analysis of 15 prospective studies, higher levels of IL-6 (per log pg/mL) were significantly associated with a higher risk of incident T2D (1·24 95% CI, 1·17, 1·32; P = 1 × 10-12). In a trans-ethnic meta-analysis of 260,614 cases and 1,350,640 controls, the IL6R Asp358Ala missense variant was associated with lower odds of T2D (OR, 0·98; 95% CI, 0·97, 0·99; P = 2 × 10-7). This association was not due to diagnostic misclassification and was consistent across ethnic groups. IL-6 levels mediated up to 5% of the association between higher body mass index and T2D.

INTERPRETATION

Large-scale human prospective and genetic data provide evidence that IL-6 mediated inflammation is implicated in the etiology of T2D but suggest that the impact of this pathway on disease risk in the general population is likely to be small.

FUNDING

The EPICNorfolk study has received funding from the Medical Research Council (MRC) (MR/N003284/1, MC-UU_12015/1 and MC_PC_13048) and Cancer Research UK (C864/A14136). The Fenland Study is funded by the MRC (MC_UU_12015/1 and MC_PC_13046).

TBC1D1 interacting proteins, VPS13A and VPS13C, regulate GLUT4 homeostasis in C2C12 myotubes

Proteins involved in the spaciotemporal regulation of GLUT4 trafficking represent potential therapeutic targets for the treatment of insulin resistance and type 2 diabetes. A key regulator of insulin- and exercise-stimulated glucose uptake and GLUT4 trafficking is TBC1D1. This study aimed to identify proteins that regulate GLUT4 trafficking and homeostasis via TBC1D1. Using an unbiased quantitative proteomics approach, we identified proteins that interact with TBC1D1 in C2C12 myotubes including VPS13A and VPS13C, the Rab binding proteins EHBP1L1 and MICAL1, and the calcium pump SERCA1. These proteins associate with TBC1D1 via its phosphotyrosine binding (PTB) domains and their interactions with TBC1D1 were unaffected by AMPK activation, distinguishing them from the AMPK regulated interaction between TBC1D1 and AMPKα1 complexes. Depletion of VPS13A or VPS13C caused a post-transcriptional increase in cellular GLUT4 protein and enhanced cell surface GLUT4 levels in response to AMPK activation. The phenomenon was specific to GLUT4 because other recycling proteins were unaffected. Our results provide further support for a role of the TBC1D1 PTB domains as a scaffold for a range of Rab regulators, and also the VPS13 family of proteins which have been previously linked to fasting glycaemic traits and insulin resistance in genome wide association studies.