Phenotypic and Genetic Characterization of Lower LDL Cholesterol and Increased Type 2 Diabetes Risk in the UK Biobank

Minor Genetic Overlap Among Rheumatoid Arthritis, Myocardial Infarction, and Myocardial Infarction Risk Determinants

OBJECTIVE

The aim of this study was to investigate whether a shared genetic susceptibility exists between individuals with rheumatoid arthritis (RA) and individuals with myocardial infarction (MI)-including major MI risk factors-and to quantify the degree of any such overlap.

METHODS

Genome-wide association study (GWAS) data for individuals with RA were constructed from a sample of 26,637 Swedish patients with RA and controls without RA. For patients with MI, GWAS data were obtained from a previously published meta-analysis. Genome-wide genetic correlation was estimated via linkage disequilibrium score regression. LAVA was employed to estimate local genetic correlations in ~2,500 nonoverlapping loci, including the major histocompatibility complex. The controls without RA were used for reference panel data. We also assessed stratified estimates of both genome-wide and local genetic correlation based on subsamples of individuals with seropositive RA and those with seronegative RA. Furthermore, genome-wide genetic correlation was estimated between RA and selected cardiovascular risk factors to elucidate pleiotropic relationships.

RESULTS

Following quality control, our GWAS of patients with RA consisted of 25,826 individuas. Genome-wide genetic correlation between patients with RA and MI was estimated to 0.13 (95% confidence interval -0.03 to 0.29). Six regions exhibited significant local genetic correlation, though none harbored any known risk single-nucleotide polymorphisms for either of the two traits. Estimates were similar in both individuals with seropositive RA and those with seronegative RA. No statistically significant genetic correlations were observed between RA risk factors and any of the MI risk factors.

CONCLUSION

Our findings indicate that genetic overlap between patients with RA and MI is minor. Furthermore, genetic overlap between RA and MI risk factors seem unlikely to provide a major contribution to the increased risk of MI observed in patients with RA.

Genome-wide association study of exercise-induced skeletal muscle hypertrophy and the construction of predictive model

The aim of the current study was to investigate interindividual differences in muscle thickness of the rectus femoris (MTRF) following 12 wk of resistance training (RT) or high-intensity interval training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-wk exercise period. A genome-wide association study was used to identify variants associated with the MTRF response, separately for RT and HIIT. Using the polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for the MTRF response were constructed using random forest (RF), support vector mac (SVM), and generalized linear model (GLM) in 10 cross-validated approaches. MTRF increased significantly after both RT (8.8%, P < 0.05) and HIIT (5.3%, P < 0.05), but with considerable interindividual differences (RT: -13.5 to 38.4%, HIIT: -14.2 to 30.7%). Eleven lead single-nucleotide polymorphisms in RT and eight lead single-nucleotide polymorphisms in HIIT were identified at a significance level of P < 1 × 10-5. The PPS was associated with the MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance compared with RF models (P < 0.05), and the GLM demonstrated optimal performance with an area under curve of 0.809 (95% confidence interval: 0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.NEW & NOTEWORTHY The interindividual variability induced muscle hypertrophy by resistance training (RT) or high-intensity interval training (HIIT) and the associated genetic architecture remain uncertain. We identified genetic variants that underlie RT- or HIIT-induced muscle hypertrophy and established them as pivotal factors influencing the response regardless of the training type. The genetic-phenotype predictive model developed has the potential to identify nonresponders or individuals with low responsiveness before engaging in exercise training.

Metabolic factors are not the direct mediators of the association between type 2 diabetes and osteoporosis

Objective

The causal relationship between type 2 diabetes mellitus (T2DM) and osteoporosis (OS) remains unclear. This study aims to investigate the causal relationship and explore the potential metabolic mechanism and its mediating role.

Methods

We conducted a comprehensive study, gathering data on 490,089 T2DM patients from the genome-wide association study (GWAS) database and selecting OS data from FinnGen and MRC-IEU sources, including 212,778 and 463,010 patients, respectively, for causal analysis. Simultaneously, we explored the potential roles of three obesity traits and 30 metabolic and inflammation-related mediating variables in the causal relationship.

Results

There is a strong causal relationship between T2DM and OS. The data from our two different database sources appeared in the same direction, but after correcting for body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR), the direction became the same. T2DM may increase the risk of OS [odds ratio (OR) > 1.5, p < 0.001]. Steiger's test results show that there is no reverse causality. No risk factors related to glycolipid metabolism, amino acid metabolism, and inflammation were found to mediate the causal relationship.

Conclusion

This study's findings indicate a robust causal relationship between T2DM and OS, influenced by relevant factors such as BMI. Our results shed light on the pathogenesis of OS and underscore the importance for clinicians to treat metabolic disorders to prevent osteoporosis.

Regulation of Mitochondria-Derived Immune Activation by 'Antiviral' TRIM Proteins

Mitochondria are key orchestrators of antiviral responses that serve as platforms for the assembly and activation of innate immune-signaling complexes. In response to viral infection, mitochondria can be triggered to release immune-stimulatory molecules that can boost interferon production. These same molecules can be released by damaged mitochondria to induce pathogenic, antiviral-like immune responses in the absence of infection. This review explores how members of the tripartite motif-containing (TRIM) protein family, which are recognized for their roles in antiviral defense, regulate mitochondria-based innate immune activation. In antiviral defense, TRIMs are essential components of immune signal transduction pathways and function as directly acting viral restriction factors. TRIMs carry out conceptually similar activities when controlling immune activation related to mitochondria. First, they modulate immune-signaling pathways that can be activated by mitochondrial molecules. Second, they co-ordinate the direct removal of mitochondria and associated immune-activating factors through mitophagy. These insights broaden the scope of TRIM actions in innate immunity and may implicate TRIMs in diseases associated with mitochondria-derived inflammation.

Lipid levels and low back pain risk: A two-sample mendelian randomization study

BACKGROUND

Previous observational studies have shown controversial results about the relationship between lipid levels and low back pain (LBP). Herein, we aimed to explore the potential causal relationship between lipid levels and LBP by using the mendelian randomization (MR) analysis.

METHODS

In this two-sample MR study, data were extracted from publicly available MRC Integrative Epidemiology Unit database. Three single-nucleotide polymorphisms (SNPs) of lipid levels [high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), and triglycerides (TG)] and two SNPs of LBP risk (LBP and back pain) were retrieved and used as genetic instrumental variables. Inverse-variance weighted (IVW), weighted median, MR-Egger, robust adjusted profile score (MR-RAPS), and MR-PRESSO were used to examine the potential causal association between lipid levels and LBP.

RESULTS

IVW (fixed effect) estimation indicated that increased HDL-C level was negatively related to the odds of LBP for European populations. [odds ratio (OR) = 0.923, 95% confidence interval (CI): 0.857-0.993, P = 0.0323]. Similar results were also found in IVW (random effect) (OR = 0.923, 95% CI: 0.866-0.983, P = 0.0134), MR-Egger (OR = 0.858, 95%CI: 0.757-0.973, P = 0.0177), MR-RAPS (OR = 0.932, 95%CI: 0.871-0.997, P = 0.0419), and MR-PRESSO (OR = 0.933, 95%CI: 0.880-0.989, P = 0.0198) analyses. Whereas no causal link was observed between LDL-C/TG and LBP risk (P>0.05).

CONCLUSION

This two-sample MR study demonstrated a causal relationship between lipid levels and LBP risk. Further investigations are necessary to elucidate the causal association and specific underlying mechanisms by which lipid levels contribute to the development of LBP.

Single-Cell Dissection of the Immune Response After Acute Myocardial Infarction

BACKGROUND

The immune system's role in ST-segment-elevated myocardial infarction (STEMI) remains poorly characterized but is an important driver of recurrent cardiovascular events. While anti-inflammatory drugs show promise in reducing recurrence risk, their broad immune system impairment may induce severe side effects. To overcome these challenges, a nuanced understanding of the immune response to STEMI is needed.

METHODS

For this, we compared peripheral blood mononuclear single-cell RNA-sequencing (scRNA-seq) and plasma protein expression over time (hospital admission, 24 hours, and 6-8 weeks post-STEMI) in 38 patients and 38 controls (95 995 diseased and 33 878 control peripheral blood mononuclear cells).

RESULTS

Compared with controls, classical monocytes were increased and CD56dim natural killer cells were decreased in patients with STEMI at admission and persisted until 24 hours post-STEMI. The largest gene expression changes were observed in monocytes, associating with changes in toll-like receptor, interferon, and interleukin signaling activity. Finally, a targeted cardiovascular biomarker panel revealed expression changes in 33/92 plasma proteins post-STEMI. Interestingly, interleukin-6R, MMP9 (matrix metalloproteinase-9), and LDLR (low-density lipoprotein receptor) were affected by coronary artery disease-associated genetic risk variation, disease status, and time post-STEMI, indicating the importance of considering these aspects when defining potential future therapies.

CONCLUSIONS

Our analyses revealed the immunologic pathways disturbed by STEMI, specifying affected cell types and disease stages. Additionally, we provide insights into patients expected to benefit most from anti-inflammatory treatments by identifying the genetic variants and disease stage at which these variants affect the outcome of these (drug-targeted) pathways. These findings advance our knowledge of the immune response post-STEMI and provide guidance for future therapeutic studies.

Genetically identified mediators associated with increased risk of stroke and cardiovascular disease in individuals with autism spectrum disorder

Growing evidence suggested that individuals with autism spectrum disorder (ASD) associated with stroke and cardiovascular disease (CVD). However, the causal association between ASD and the risk of stroke and CVD remains unclear. To validate this, we performed two-sample Mendelian randomization (MR) and two-step mediation MR analyses, using relevant genetic variants sourced from the largest genome-wide association studies (GWASs). Two-sample MR evidence indicated causal relationships between ASD and any stroke (OR = 1.1184, 95% CI: 1.0302-1.2142, P < 0.01), ischemic stroke (IS) (OR = 1.1157, 95% CI: 1.0237-1.2160, P = 0.01), large-artery atherosclerotic stroke (LAS) (OR = 1.2902, 95% CI: 1.0395-1.6013, P = 0.02), atrial fibrillation (AF) (OR = 1.0820, 95% CI: 1.0019-1.1684, P = 0.04), and heart failure (HF) (OR = 1.1018, 95% CI: 1.0007-1.2132, P = 0.05). Additionally, two-step mediation MR suggested that type 2 diabetes mellitus (T2DM) partially mediated this effect (OR = 1.14, 95%CI: 1.02-1.28, P = 0.03). The mediated proportion were 10.96% (95% CI: 0.58%-12.10%) for any stroke, 11.77% (95% CI: 10.58%-12.97%) for IS, 10.62% (95% CI: 8.04%-13.20%) for LAS, and 7.57% (95% CI: 6.79%-8.36%) for HF. However, no mediated effect was observed between ASD and AF risk. These findings have implications for the development of prevention strategies and interventions for stroke and CVD in patients with ASD.

HMG-CoA reductase is a potential therapeutic target for migraine: a mendelian randomization study

Statins are thought to have positive effects on migraine but existing data are inconclusive. We aimed to evaluate the causal effect of such drugs on migraines using Mendelian randomization. We used four types of genetic instruments as proxies for HMG-CoA reductase inhibition. We included the expression quantitative trait loci of the HMG-CoA reductase gene and genetic variation within or near the HMG-CoA reductase gene region. Variants were associated with low-density lipoprotein cholesterol, apolipoprotein B, and total cholesterol. Genome-wide association study summary data for the three lipids were obtained from the UK Biobank. Comparable data for migraine were obtained from the International Headache Genetic Consortium and the FinnGen Consortium. Inverse variance weighting method was used for the primary analysis. Additional analyses included pleiotropic robust methods, colocalization, and meta-analysis. Genetically determined high expression of HMG-CoA reductase was associated with an increased risk of migraines (OR = 1.55, 95% CI 1.30-1.84, P = 6.87 × 10-7). Similarly, three genetically determined HMG-CoA reductase-mediated lipids were associated with an increased risk of migraine. These conclusions were consistent across meta-analyses. We found no evidence of bias caused by pleiotropy or genetic confounding factors. These findings support the hypothesis that statins can be used to treat migraine.

Blood lipid levels mediating the effects of sex hormone-binding globulin on coronary heart disease: Mendelian randomization and mediation analysis

Observational studies indicate that serum sex hormone-binding globulin (SHBG) levels are inversely correlated with blood lipid levels and coronary heart disease (CHD) risk. Given that dyslipidemia is an established risk factor for CHD, we aim to employ Mendelian randomization (MR) in conjunction with mediation analysis to confirm the mediating role of blood lipid levels in the association between SHBG and CHD. First, we assessed the causality between serum SHBG levels and five cardiovascular diseases using univariable MR. The results revealed causality between SHBG levels and reduced risk of CHD, myocardial infarction, as well as hypertension. Specifically, the most significant reduction was observed in CHD risk, with an odds ratio of 0.73 (95% CI 0.63-0.86) for each one-standard-deviation increase in SHBG. The summary-level data of serum SHBG levels and CHD are derived from a sex-specific genome-wide association study (GWAS) conducted by UK Biobank (sample size = 368,929) and a large-scale GWAS meta-analysis (60,801 cases and 123,504 controls), respectively. Subsequently, we further investigated the mediating role of blood lipid level in the association between SHBG and CHD. Mediation analysis clarified the mediation proportions for four mediators: high cholesterol (48%), very low-density lipoprotein cholesterol (25.1%), low-density lipoprotein cholesterol (18.5%), and triglycerides (44.3%). Summary-level data for each mediator were sourced from the UK Biobank and publicly available GWAS. The above results confirm negative causality between serum SHBG levels and the risk of CHD, myocardial infarction, and hypertension, with the causal effect on reducing CHD risk largely mediated by the improvement of blood lipid profiles.

The causal relationship between metabolic syndrome and its components and cardiovascular disease: A mendelian randomization study

AIM

To investigate the causal relationship between metabolic syndrome (MetS) and its components and 14 cardiovascular diseases using Mendelian randomization (MR).

METHODS

We used summary statistics from large-scale genome-wide association studies of MetS, its components, and cardiovascular diseases. We performed a two-sample MR analysis using the inverse-variance weighted method and other sensitivity methods. We also performed multivariate MR to adjust for potential risk factors.

RESULTS

Our study found that MetS was causally associated with an increased risk of ischemic stroke, abdominal aortic aneurysm, pulmonary embolism, coronary heart disease, heart failure, and peripheral artery disease. Waist circumference was causally associated with an increased risk of 6 cardiovascular diseases. Type 2 diabetes mellitus, diastolic blood pressure, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol were all causally associated with coronary heart disease, with varying causal relationships with the remaining 5 cardiovascular diseases. Multivariate MR showed that, except for ischaemic stroke, waist circumference remained causally associated with the remaining five cardiovascular diseases after adjusting for potential confounders.

CONCLUSION

Our study provides evidence that metabolic syndrome is causally associated with 6 cardiovascular diseases. Waist circumference is the most important component of these relationships. These findings have implications for the prevention and management of metabolic syndrome and cardiovascular diseases.

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.

The causal associations of circulating lipids with Barrett's Esophagus and Esophageal Cancer: a bi-directional, two sample mendelian randomization analysis

OBJECTIVE

The causal associations of circulating lipids with Barrett's Esophagus (BE) and Esophageal Cancer (EC) has been a topic of debate. This study sought to elucidate the causality between circulating lipids and the risk of BE and EC.

METHODS

We conducted two-sample Mendelian randomization (MR) analyses using single nucleotide polymorphisms (SNPs) of circulating lipids (n = 94,595 - 431,167 individuals), BE (218,792 individuals), and EC (190,190 individuals) obtained from the publicly available IEU OpenGWAS database. The robustness and reliability of the results were ensured by employing inverse-variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO methods. The presence of horizontal pleiotropy, heterogeneities, and stability of instrumental variables were assessed through MR-Egger intercept test, Cochran's Q test, and leave-one-out sensitivity analysis. Additionally, bidirectional MR and multivariable MR (MVMR) were performed to explore reverse causality and adjust for known confounders, respectively.

RESULTS

None of the testing methods revealed statistically significant horizontal pleiotropy, directional pleiotropy, or heterogeneity. Univariate MR analyses using IVW indicated a robust causal relationship between increased triglycerides and BE (odds ratio [OR] = 1.79, p-value = 0.009), while no significant association with EC was observed. Inverse MR analysis indicated no evidence of reverse causality in the aforementioned outcomes. In MVMR analyses, elevated triglycerides (TRG) were significantly and positively associated with BE risk (OR = 1.79, p-value = 0.041).

CONCLUSION

This MR study suggested that genetically increased triglycerides were closely related to an elevated risk of BE, potentially serving as a biomarker for the diagnosis of BE in the future.

Association between basal metabolic rate and cardio-metabolic risk factors: Evidence from a Mendelian Randomization study

Background

Cardio-metabolic risk factors play a crucial role in the development of cardiovascular and metabolic diseases. Basal metabolic rate (BMR) is a fundamental physiological parameter that affects energy expenditure and might contribute to variations in these risk factors. However, the exact relationship between BMR and cardio-metabolic risk factors has remained unclear.

Methods

We employed Mendelian Randomization (MR) analysis to explore the association between BMR (N: 534,045) and various cardio-metabolic risk factors, including body mass index (BMI, N: 681,275), fasting glucose (N: 200,622), high-density lipoprotein (HDL) cholesterol (N = 403,943), low-density lipoprotein (LDL) cholesterol (N = 431,167), total cholesterol (N: 344,278), and triglycerides (N: 441,016), C-reactive protein (N: 436,939), waist circumference (N: 232,101), systolic blood pressure (N: 810,865), diastolic blood pressure (N: 810,865), glycated haemoglobin (N: 389,889), and N-terminal prohormone brain natriuretic peptide (N: 21,758). We leveraged genetic variants strongly associated with BMR as instrumental variables to investigate potential causal relationships, with the primary analysis using the Inverse Variance Weighted (IVW) method.

Results

Our MR analysis revealed compelling evidence of a causal link between BMR and specific cardio-metabolic risk factors. Specifically, genetically determined higher BMR was associated with an increased BMI (β = 0.7538, 95% confidence interval [CI]: 0.6418 to 0.8659, p < 0.001), lower levels of HDL cholesterol (β = -0.3293, 95% CI: 0.4474 to -0.2111, p < 0.001), higher levels of triglycerides (β = 0.1472, 95% CI: 0.0370 to 0.2574, p = 0.0088), waist circumference (β = 0.4416, 95% CI: 0.2949 to 0.5883, p < 0.001), and glycated haemoglobin (β = 0.1037, 95% CI: 0.0080 to 0.1995, p = 0.0377). However, we did not observe any significant association between BMR and fasting glucose, LDL cholesterol, total cholesterol, C-reactive protein, systolic blood pressure, diastolic blood pressure, or N-terminal prohormone brain natriuretic peptide (all p-values>0.05).

Conclusion

This MR study provides valuable insights into the relationship between BMR and cardio-metabolic risk factors. Understanding the causal links between BMR and these factors could have important implications for the development of targeted interventions and therapies.

Maternal history of Alzheimer's disease predisposes to altered serum cholesterol levels in adult offspring

Controversial findings regarding the association between serum cholesterol levels and Alzheimer's disease (AD) have been identified through observational studies. The genetic basis shared by both factors and the causality between them remain largely unknown. The objective of this study is to examine the causal impact of maternal history of AD on changes in serum cholesterol levels in adult offspring. By retrieving genetic variants from summary statistics of large-scale genome-wide association study of maternal history of AD (European-based: Ncase = 27 696, Ncontrol = 260 980). The causal association between genetically predicted maternal history of AD and changes in serum cholesterol levels in adult offspring was examined using the two-sample Mendelian randomization (MR) method. Causal impact estimates were calculated using single-nucleotide polymorphisms in both univariable MR (UMR) and multivariable MR (MVMR) analyses. Additionally, other approaches, such as Cochran's Q test and leave-one-out variant analysis, were employed to correct for potential biases. The results of UMR presented that genetically predicted maternal history of AD was positively associated with hypercholesterolemia (OR = 1.014; 95% CI: 1.009-1.018; p < 0.001), total cholesterol (OR = 1.29; 95% CI: 1.134-1.466; p < 0.001) and low-density lipoprotein (OR = 1.525; 95% CI: 1.272-1.828; p < 0.001) among adult offspring. Genetic predisposition for maternal history of AD to be negatively associated with high-density lipoprotein (OR = 0.889; 95% CI: 0.861-0.917; p < 0.001). The MVMR analysis remained robust and significant after adjusting for diabetes and obesity in offspring. Sufficient evidence was provided in this study to support the putative causal impact of maternal history of AD on the change of serum cholesterol profile in adult offspring. In clinical practice, priority should be given to the detection and monitoring of cholesterol levels in individuals with a maternal history of AD, particularly in the early stages.

Age effect on the shared etiology of glycemic traits and serum lipids: evidence from a Chinese twin study

PURPOSE

Diabetes and dyslipidemia are among the most common chronic diseases with increasing global disease burdens, and they frequently occur together. The study aimed to investigate differences in the heritability of glycemic traits and serum lipid indicators and differences in overlapping genetic and environmental influences between them across age groups.

METHODS

This study included 1189 twin pairs from the Chinese National Twin Registry and divided them into three groups: aged ≤ 40, 41-50, and > 50 years old. Univariate and bivariate structural equation models (SEMs) were conducted on glycemic indicators and serum lipid indicators, including blood glucose (GLU), glycated hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), in the total sample and three age groups.

RESULTS

All phenotypes showed moderate to high heritability (0.37-0.64). The heritability of HbA1c demonstrated a downward trend with age (HbA1c: 0.50-0.79), while others remained relatively stable (GLU: 0.55-0.62, TC: 0.58-0.66, TG: 0.50-0.63, LDL-C: 0.24-0.58, HDL-C: 0.31-0.57). The bivariate SEMs demonstrated that GLU and HbA1c were correlated with each serum lipid indicator (0.10-0.17), except HDL-C. Except for HbA1c and LDL-C, as well as HbA1c and HDL-C, differences in genetic correlations underlying glycemic traits and serum lipids between age groups were observed, with the youngest group showing a significantly higher genetic correlation than the oldest group.

CONCLUSION

Across the whole adulthood, genetic influences were consistently important for GLU, TC, TG, LDL-C and HDL-C, and age may affect the shared genetic influences between glycemic traits and serum lipids. Further studies are needed to elucidate the role of age in the interactions of genes related to glycemic traits and serum lipids.

Mendelian randomization study supports the causal effects of air pollution on longevity via multiple age-related diseases

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may reduce life expectancy; however, the causal pathways of PM2.5 exposure affecting life expectancy remain unknown. Here, we assess the causal effects of genetically predicted PM2.5 concentration on common chronic diseases and longevity using a Mendelian randomization (MR) statistical framework based on large-scale genome-wide association studies (GWAS) (>400,000 participants). After adjusting for other types of air pollution and smoking, we find significant causal relationships between PM2.5 concentration and angina pectoris, hypercholesterolaemia and hypothyroidism, but no causal relationship with longevity. Mediation analysis shows that although the association between PM2.5 concentration and longevity is not significant, PM2.5 exposure indirectly affects longevity via diastolic blood pressure (DBP), hypertension, angina pectoris, hypercholesterolaemia and Alzheimer's disease, with a mediated proportion of 31.5, 70.9, 2.5, 100, and 24.7%, respectively. Our findings indicate that public health policies to control air pollution may help improve life expectancy.

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation

One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data.

Potential drug targets for myocardial infarction identified through Mendelian randomization analysis and Genetic colocalization

Myocardial infarction (MI) is a major cause of death and disability worldwide, but current treatments are limited by their invasiveness, side effects, and lack of efficacy. Novel drug targets for MI prevention are urgently needed. In this study, we used Mendelian randomization to identify potential therapeutic targets for MI using plasma protein quantitative trait loci as exposure variables and MI as the outcome variable. We further validated our findings using reverse causation analysis, Bayesian co-localization analysis, and external datasets. We also constructed a protein-protein interaction network to explore the relationships between the identified proteins and known MI targets. Our analysis revealed 2 proteins, LPA and APOA5, as potential drug targets for MI, with causal effects on MI risk confirmed by multiple lines of evidence. LPA and APOA5 are involved in lipid metabolism and interact with target proteins of current MI medications. We also found 4 other proteins, IL1RN, FN1, NT5C, and SEMA3C, that may have potential as drug targets but require further confirmation. Our study demonstrates the utility of Mendelian randomization and protein quantitative trait loci in discovering novel drug targets for complex diseases such as MI. It provides insights into the underlying mechanisms of MI pathology and treatment.

Investigating linear and nonlinear associations of LDL cholesterol with incident chronic kidney disease, atherosclerotic cardiovascular disease and all-cause mortality: A prospective and Mendelian randomization study

BACKGROUND AND AIMS

Observational studies suggest potential nonlinear associations of low-density lipoprotein cholesterol (LDL-C) with cardio-renal diseases and mortality, but the causal nature of these associations is unclear. We aimed to determine the shape of causal relationships of LDL-C with incident chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality, and to evaluate the absolute risk of adverse outcomes contributed by LDL-C itself.

METHODS

Observational analysis and one-sample Mendelian randomization (MR) with linear and nonlinear assumptions were performed using the UK Biobank of >0.3 million participants with no reported prescription of lipid-lowering drugs. Two-sample MR on summary-level data from the Global Lipid Genetics Consortium (N = 296,680) and the CKDGen (N = 625,219) was employed to replicate the relationship for kidney traits. The 10-year probabilities of the outcomes was estimated by integrating the MR and Cox models.

RESULTS

Observationally, participants with low LDL-C were significantly associated with a decreased risk of ASCVD, but an increased risk of CKD and all-cause mortality. Univariable MR showed an inverse total effect of LDL-C on incident CKD (HR [95% CI]:0.84 [0.73-0.96]; p = 0.011), a positive effect on ASCVD (1.41 [1.29-1.53]; p<0.001), and no significant causal effect on all-cause mortality. Multivariable MR, controlling for high-density lipoprotein cholesterol (HDL-C) and triglycerides, identified a positive direct effect on ASCVD (1.32 [1.18-1.47]; p<0.001), but not on CKD and all-cause mortality. These results indicated that genetically predicted low LDL-C had an inverse indirect effect on CKD mediated by HDL-C and triglycerides, which was validated by a two-sample MR analysis using summary-level data from the Global Lipid Genetics Consortium (N = 296,680) and the CKDGen consortium (N = 625,219). Suggestive evidence of a nonlinear causal association between LDL-C and CKD was found. The 10-year probability curve showed that LDL-C concentrations below 3.5 mmol/L were associated with an increased risk of CKD.

CONCLUSIONS

In the general population, lower LDL-C was causally associated with lower risk of ASCVD, but appeared to have a trade-off for an increased risk of CKD, with not much effect on all-cause mortality. LDL-C concentration below 3.5 mmol/L may increase the risk of CKD.

Leveraging information between multiple population groups and traits improves fine-mapping resolution

Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.

The importance of polymorphisms in the genes encoding glutathione S-transferase isoenzymes in development of selected cancers and cardiovascular diseases

Glutathione S-transferases are a family of enzymes, whose main role is to detoxify cells from many exogenous factors, such as xenobiotics or carcinogens. It has also been proven that changes in the genes encoding these enzymes may affect the incidence of selected cancers and cardiovascular diseases. The aim of this study was to review the most important reports related to the role of glutathione S-transferases in the pathophysiology of two of the most common diseases in modern society - cancers and cardiovascular diseases. It was shown that polymorphisms in the genes encoding glutathione S-transferases are associated with the development of these diseases. However, depending on the ethnic group, the researchers obtained divergent results related to this field. In the case of the GSTP1 A/G gene polymorphism was shown an increased incidence of breast cancer in Asian women, while this relationship in European and African women was not found. Similarly. In the case of cardiovascular diseases, the differences in the influence of GSTM1, GSTT1, GSTP1 and GSTA1 polymorphisms on their development or lack of it depending on the continent were shown. These examples show that the development of the above-mentioned diseases is not only influenced by genetic changes, but their pathophysiology is more complex. The mere presence of a specific genotype within a studied polymorphism may not predispose to cancer, but in combination with environmental factors, which often depend on the place of residence, it may elevate the chance of developing the selected disease.

Beyond apples and pears: sex-specific genetics of body fat percentage

Introduction

Biological sex influences both overall adiposity and fat distribution. Further, testosterone and sex hormone binding globulin (SHBG) influence adiposity and metabolic function, with differential effects of testosterone in men and women. Here, we aimed to perform sex-stratified genome-wide association studies (GWAS) of body fat percentage (BFPAdj) (adjusting for testosterone and sex hormone binding globulin (SHBG)) to increase statistical power.

Methods

GWAS were performed in white British individuals from the UK Biobank (157,937 males and 154,337 females). To avoid collider bias, loci associated with SHBG or testosterone were excluded. We investigated association of BFPAdj loci with high density cholesterol (HDL), triglyceride (TG), type 2 diabetes (T2D), coronary artery disease (CAD), and MRI-derived abdominal subcutaneous adipose tissue (ASAT), visceral adipose tissue (VAT) and gluteofemoral adipose tissue (GFAT) using publicly available data from large GWAS. We also performed 2-sample Mendelian Randomization (MR) using identified BFPAdj variants as instruments to investigate causal effect of BFPAdj on HDL, TG, T2D and CAD in males and females separately.

Results

We identified 195 and 174 loci explaining 3.35% and 2.60% of the variation in BFPAdj in males and females, respectively at genome-wide significance (GWS, p<5x10-8). Although the direction of effect at these loci was generally concordant in males and females, only 38 loci were common to both sexes at GWS. Seven loci in males and ten loci in females have not been associated with any adiposity/cardiometabolic traits previously. BFPAdj loci generally did not associate with cardiometabolic traits; several had paradoxically beneficial cardiometabolic effects with favourable fat distribution. MR analyses did not find convincing supportive evidence that increased BFPAdj has deleterious cardiometabolic effects in either sex with highly significant heterogeneity.

Conclusions

There was limited genetic overlap between BFPAdj in males and females at GWS. BFPAdj loci generally did not have adverse cardiometabolic effects which may reflect the effects of favourable fat distribution and cardiometabolic risk modulation by testosterone and SHBG.

Association between serum LDL-C concentrations and risk of diabetes: A prospective cohort study

BACKGROUND

Low-density lipoprotein cholesterol (LDL-C) and diabetes mellitus are both modifiable risk factors for cardiovascular disease; however, whether elevated LDL-C levels confer a risk for diabetes remains unclear.

OBJECTIVE

We aimed to examine the association between serum LDL-C concentrations at baseline and the risk of developing diabetes at follow-up in the general population of Chinese adults.

METHODS

This study included 5274 adults aged ≥ 40 years from a community cohort who were without diabetes and followed for a median of 4.4 years. A standard 75-g oral glucose tolerance test was conducted at baseline and follow-up visits to diagnose diabetes. Logistic regression models and a restricted cubic spline were used to examine the association between baseline serum LDL-C levels and the risk of diabetes development. Subgroup analyses were conducted stratifying on age, sex, body mass index, hypertension, family history of diabetes, and LDL-C levels.

RESULTS

A total of 652 participants (12%) developed diabetes during the follow-up period. Compared to quartile 1 of serum LDL-C, quartiles 2, 3, and 4 were associated with a 30%, 33%, and 30% significantly higher risk of diabetes, respectively after adjustment for confounders including homeostatic model assessment for insulin resistance. The linear relationship between baseline LDL-C down to 30.1 mg/dL and incident diabetes was demonstrated by restricted cubic spline analysis, and each 1-SD increase in LDL-C concentration (28.5 mg/dL) was associated with a 12% increase in the risk of diabetes (odds ratio 1.12, 95% confidence interval 1.03-1.22).

CONCLUSION

In this community-based general population, higher serum LDL-C levels were linearly associated with an elevated risk of incident diabetes.

Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies

Local genetic correlation evaluates the correlation of additive genetic effects between different traits across the same genetic variants at a genomic locus. It has been proven informative for understanding the genetic similarities of complex traits beyond that captured by global genetic correlation calculated across the whole genome. Several summary-statistics-based approaches have been developed for estimating local genetic correlation, including $\rho$-hess, SUPERGNOVA and LAVA. However, there has not been a comprehensive evaluation of these methods to offer practical guidelines on the choices of these methods. In this study, we conduct benchmark comparisons of the performance of these three methods through extensive simulation and real data analyses. We focus on two technical difficulties in estimating local genetic correlation: sample overlaps across traits and local linkage disequilibrium (LD) estimates when only the external reference panels are available. Our simulations suggest the likelihood of incorrectly identifying correlated regions and local correlation estimation accuracy are highly dependent on the estimation of the local LD matrix. These observations are corroborated by real data analyses of 31 complex traits. Overall, our findings illuminate the distinct results yielded by different methods applied in post-genome-wide association studies (post-GWAS) local correlation studies. We underscore the sensitivity of local genetic correlation estimates and inferences to the precision of local LD estimation. These observations accentuate the vital need for ongoing refinement in methodologies.

A transcriptomic and proteomic atlas of obesity and type 2 diabetes in cynomolgus monkeys

Obesity and type 2 diabetes (T2D) remain major global healthcare challenges, and developing therapeutics necessitates using nonhuman primate models. Here, we present a transcriptomic and proteomic atlas of all the major organs of cynomolgus monkeys with spontaneous obesity or T2D in comparison to healthy controls. Molecular changes occur predominantly in the adipose tissues of individuals with obesity, while extensive expression perturbations among T2D individuals are observed in many tissues such as the liver and kidney. Immune-response-related pathways are upregulated in obesity and T2D, whereas metabolism and mitochondrial pathways are downregulated. Moreover, we highlight some potential therapeutic targets, including SLC2A1 and PCSK1 in obesity as well as SLC30A8 and SLC2A2 in T2D. Our study provides a resource for exploring the complex molecular mechanism of obesity and T2D and developing therapies for these diseases, with limitations including lack of hypothalamus, isolated islets of Langerhans, longitudinal data, and body fat percentage.

Genetic Variants of Adrenoceptors

Adrenoceptors are class A G-protein-coupled receptors grouped into three families (α1-, α2-, and β-adrenoceptors), each one including three members. All nine corresponding adrenoceptor genes display genetic variation in their coding and adjacent non-coding genomic region. Coding variants, i.e., nucleotide exchanges within the transcribed and translated receptor sequence, may result in a difference in amino acid sequence thus altering receptor function and signaling. Such variants have been intensely studied in vitro in overexpression systems and addressed in candidate-gene studies for distinct clinical parameters. In recent years, large cohorts were analyzed in genome-wide association studies (GWAS), where variants are detected as significant in context with specific traits. These studies identified two of the in-depth characterized 18 coding variants in adrenoceptors as repeatedly statistically significant genetic risk factors - p.Arg389Gly in the β1- and p.Thr164Ile in the β2-adrenoceptor, along with 56 variants in the non-coding regions adjacent to the adrenoceptor gene loci, the functional role of which is largely unknown at present. This chapter summarizes current knowledge on the two coding variants in adrenoceptors that have been consistently validated in GWAS and provides a prospective overview on the numerous non-coding variants more recently attributed to adrenoceptor gene loci.

Investigating causality in the association between DNA methylation and type 2 diabetes using bidirectional two-sample Mendelian randomisation

AIMS/HYPOTHESIS

Several studies have identified associations between type 2 diabetes and DNA methylation (DNAm). However, the causal role of these associations remains unclear. This study aimed to provide evidence for a causal relationship between DNAm and type 2 diabetes.

METHODS

We used bidirectional two-sample Mendelian randomisation (2SMR) to evaluate causality at 58 CpG sites previously detected in a meta-analysis of epigenome-wide association studies (meta-EWAS) of prevalent type 2 diabetes in European populations. We retrieved genetic proxies for type 2 diabetes and DNAm from the largest genome-wide association study (GWAS) available. We also used data from the Avon Longitudinal Study of Parents and Children (ALSPAC, UK) when associations of interest were not available in the larger datasets. We identified 62 independent SNPs as proxies for type 2 diabetes, and 39 methylation quantitative trait loci as proxies for 30 of the 58 type 2 diabetes-related CpGs. We applied the Bonferroni correction for multiple testing and inferred causality based on p<0.001 for the type 2 diabetes to DNAm direction and p<0.002 for the opposing DNAm to type 2 diabetes direction in the 2SMR analysis.

RESULTS

We found strong evidence of a causal effect of DNAm at cg25536676 (DHCR24) on type 2 diabetes. An increase in transformed residuals of DNAm at this site was associated with a 43% (OR 1.43, 95% CI 1.15, 1.78, p=0.001) higher risk of type 2 diabetes. We inferred a likely causal direction for the remaining CpG sites assessed. In silico analyses showed that the CpGs analysed were enriched for expression quantitative trait methylation sites (eQTMs) and for specific traits, dependent on the direction of causality predicted by the 2SMR analysis.

CONCLUSIONS/INTERPRETATION

We identified one CpG mapping to a gene related to the metabolism of lipids (DHCR24) as a novel causal biomarker for risk of type 2 diabetes. CpGs within the same gene region have previously been associated with type 2 diabetes-related traits in observational studies (BMI, waist circumference, HDL-cholesterol, insulin) and in Mendelian randomisation analyses (LDL-cholesterol). Thus, we hypothesise that our candidate CpG in DHCR24 may be a causal mediator of the association between known modifiable risk factors and type 2 diabetes. Formal causal mediation analysis should be implemented to further validate this assumption.

High doses of rosuvastatin induce impaired branched-chain amino acid catabolism and lead to insulin resistance

NEW FINDINGS

What is the central question of this study? Is there a risk of developing diabetes associated with statin treatment? What is the underlying mechanism of the increased incidence rate of new-onset diabetes in patients treated with rosuvastatin? What is the main finding and its importance? Rosuvastatin therapy reduced intraperitoneal glucose tolerance and changed the catabolism of branched-chain amino acid (BCAAs) in white adipose tissue and skeletal muscle. Protein phosphatase 2Cm knockdown completely abolished the effects of insulin and rosuvastatin on glucose absorption. This study provides mechanistic support for recent clinical data on rosuvastatin-related new-onset diabetes and underscores the logic for intervening in BCAA catabolism to prevent the harmful effects of rosuvastatin.

ABSTRACT

Accumulating evidence indicates that patients treated with rosuvastatin have an increased risk of developing new-onset diabetes. However, the underlying mechanism remains unclear. In this study, we administered rosuvastatin (10 mg/kg body weight) to male C57BL/6J mice for 12 weeks and found that oral rosuvastatin dramatically reduced intraperitoneal glucose tolerance. Rosuvastatin-treated mice showed considerably higher serum levels of branched-chain amino acids (BCAAs) than control mice. They also showed dramatically altered expression of BCAA catabolism-related enzymes in white adipose tissue and skeletal muscle, including downregulated mRNA expression of BCAT2 and protein phosphatase 2Cm (PP2Cm) and upregulated mRNA expression of branched-chain ketoacid dehydrogenase kinase (BCKDK). The levels of BCKD in the skeletal muscle were reduced in rosuvastatin-treated mice, which was associated with lower PP2Cm protein levels and increased BCKDK levels. We also investigated the effects of rosuvastatin and insulin administration on glucose metabolism and BCAA catabolism in C2C12 myoblasts. We observed that incubation with insulin enhanced glucose uptake and facilitated BCAA catabolism in C2C12 cells, which was accompanied by elevated Akt and glycogen synthase kinase 3 β (GSK3β) phosphorylation. These effects of insulin were prevented by co-incubation of the cells with 25 μM rosuvastatin. Moreover, the effects of insulin and rosuvastatin administration on glucose uptake and Akt and GSK3β signaling in C2C12 cells were abolished when PP2Cm was knocked down. Although the relevance of these data, obtained with high doses of rosuvastatin in mice, to therapeutic doses in humans remains to be elucidated, this study highlights a potential mechanism for the diabetogenic effects of rosuvastatin, and suggests that BCAA catabolism could be a pharmacological target for preventing the adverse effects of rosuvastatin.

Mendelian Randomization Analysis Reveals Statins Potentially Increase Amyotrophic Lateral Sclerosis Risk Independent of Peripheral Cholesterol-Lowering Effects

BACKGROUND

Observational studies suggest that statins may affect amyotrophic lateral sclerosis (ALS). However, they are limited by confounding and reverse causality biases. Therefore, we aimed to investigate the potential causal associations between statins and ALS using a mendelian randomization (MR) approach.

METHODS

Two-sample MR and drug-target MR were performed. Exposure sources included GWAS summary statistics of statin use, low-density-lipoprotein cholesterol (LDL-C), HMGCR-mediated LDL-C and LDL-C response to statins.

RESULTS

Genetic predisposition to statin medication was associated with increased ALS risk (OR = 1.085, 95% CI = 1.025-1.148, p = 0.005). After removing SNPs significantly associated with statin use from the instrumental variables (IVs), LDL-C-related higher ALS risk was absent (before removing: OR = 1.075, 95% CI = 1.013-1.141, p = 0.017; after removing: OR = 1.036, 95% CI = 0.949-1.131, p = 0.432). HMGCR-mediated LDL-C (OR = 1.033, 95% CI = 0.823-1.296, p = 0.779) and blood LDL-C response to statins (OR = 0.998, 95% CI = 0.991-1.005, p = 0.538) had no association with ALS.

CONCLUSIONS

Here, we show that statins may be a risky exposure that increases ALS risk independent of the lowering effect of LDL-C in peripheral circulation. This provides insights into ALS development and prevention.

POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes

MOTIVATION

There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals (where allelic parental origin is unknown), the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits.

RESULTS

Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation.

AVAILABILITY AND IMPLEMENTATION

The code for this method is available at https://github.com/staylorhead/POIROT-POE.

Low-density lipoprotein cholesterol levels are associated with first-phase insulin release

AIMS

Prior studies provided evidence that low-density lipoprotein (LDL)-cholesterol-lowering statins reduce cardiovascular events while conveying an increased risk of type 2 diabetes. The aim of this study was to investigate the association between LDL levels and both insulin sensitivity and insulin secretion in a cohort of 356 adult first-degree relatives of patients with type 2 diabetes.

METHODS

Insulin sensitivity was assessed by euglycemic hyperinsulinemic clamp and first-phase insulin secretion was measured by both intravenous glucose tolerance test (IVGTT) and OGTT.

RESULTS

LDL-cholesterol levels were not independently associated with insulin-stimulated glucose disposal. After adjusting for several potential confounders, LDL-cholesterol concentration exhibited a positive independent association with acute insulin response (AIR) during IVGTT and with the OGTT derived Stumvoll first-phase insulin secretion index. When insulin release was adjusted for the underlying degree of insulin sensitivity, using the disposition index (AIR × insulin-stimulated glucose disposal), β-cell function was significantly associated with LDL-cholesterol levels, even after further adjusting for several potential confounders.

CONCLUSIONS

The present results suggest that LDL cholesterol is a positive modulator of insulin secretion. The deterioration in glycemic control observed during treatment with statins might thus be explained by an impairment in insulin secretion due to the cholesterol-lowering effect of statins.

Loci for insulin processing and secretion provide insight into type 2 diabetes risk

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Distribution of lean mass and mortality risk in patients with type 2 diabetes

AIMS

The aim of the study is to evaluate the association of distribution of lean mass with the risk of all-cause mortality among patients with type 2 diabetes.

METHODS

The present cohort study included 2 335 patients with type 2 diabetes. Lean mass was assessed by dual energy X-ray absorptiometry. Cox proportional hazards regressions were used to estimate the association of lean mass distribution on the risk of mortality.

RESULTS

The average age of the patients was 58 years at baseline and 51.4% of patients were women. During a median follow-up of 4.31 years, 128 patients died. The multivariable-adjusted hazards ratios for all-cause mortality were 1.00, 1.63 (0.89-2.99), and 2.68(1.51-4.76) across the tertiles of android-to-gynoid lean mass ratio (P for trend < 0.001), respectively. The positive association of android-to-gynoid lean mass ratio with the risk of all-cause mortality was present among patients of different ages, body mass index ≥ 24 kg/m², hemoglobin A1c ≥ 7.0%, nonsmokers, men, patients using insulin, and patients with diabetes durations of more than 10 years.

CONCLUSIONS

Higher android-to-gynoid lean mass ratio, assessed by dual energy X-ray absorptiometry, was significantly associated with increased risk of all-cause mortality among patients with type 2 diabetes.

The Genetic Basis for the Increased Prevalence of Metabolic Syndrome among Post-Traumatic Stress Disorder Patients

Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can be triggered by exposure to extreme trauma. Even if PTSD is primarily a psychiatric condition, it is also characterized by adverse somatic comorbidities. One illness commonly co-occurring with PTSD is Metabolic syndrome (MetS), which is defined by a set of health risk/resilience factors including obesity, elevated blood pressure, lower high-density lipoprotein cholesterol, higher low-density lipoprotein cholesterol, higher triglycerides, higher fasting blood glucose and insulin resistance. Here, phenotypic association between PTSD and components of MetS are tested on a military veteran cohort comprising chronic PTSD presentation (n = 310, 47% cases, 83% male). Consistent with previous observations, we found significant phenotypic correlation between the various components of MetS and PTSD severity scores. To examine if this observed symptom correlations stem from a shared genetic background, we conducted genetic correlation analysis using summary statistics data from large-scale genetic studies. Our results show robust positive genetic correlation between PTSD and MetS (r_g[SE] = 0.33 [0.056], p = 4.74E-09), and obesity-related components of MetS (r_g = 0.25, SE = 0.05, p = 6.4E-08). Prioritizing genomic regions with larger local genetic correlation implicate three significant loci. Overall, these findings show significant genetic overlap between PTSD and MetS, which may in part account for the markedly increased occurrence of MetS among PTSD patients.

Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine

Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.

Increased Carotid Intima-Media Thickness in Asymptomatic Individuals Is Associated with the PCSK9 (rs2149041) Gene Polymorphism in the Mexican Mestizo Population: Results of the GEA Cohort

The increase in carotid intima-media thickness (CIMT) and coronary artery calcification (CAC) are features of subclinical atherosclerosis that might be determined by the genetic background of patients. Among the multiple risk factors, the proprotein convertase subtilisin kexin type 9 (PCSK9) has a great impact on atheroma development. Then, we focused on the potential association of the PCSK9 gene polymorphism (rs2149041) with the risk of an increased CIMT. We included 881 unrelated, asymptomatic individuals (732 normal CIMT and 149 increased CIMT) who lacked coronary calcification (CAC score = 0). Under the recessive inheritance model and adjusted by several cardiovascular risk factors, the rs2149041 polymorphism, determined by TaqMan genotyping assay, was associated with a high risk of increased CIMT (OR = 2.10, 95% IC = 1.26–3.47, P recessive = 0.004). Our results suggest that the rs2149041 polymorphism could be a risk marker for increased CIMT in asymptomatic individuals without coronary artery disease determined by the absence of a CAC score.

Shared exposure liability of type 2 diabetes and other chronic conditions in the UK Biobank

AIMS

To investigate whether the cumulative exposure risks of incident T2D are shared with other common chronic diseases.

RESEARCH DESIGN AND METHODS

We first establish and report the cross-sectional prevalence, cross-sectional co-prevalence, and incidence of seven T2D-associated chronic diseases [hypertension, atrial fibrillation, coronary artery disease, obesity, chronic obstructive pulmonary disease (COPD), and chronic kidney and liver diseases] in the UK Biobank. We use published weights of genetic variants and exposure variables to derive the T2D polygenic (PGS) and polyexposure (PXS) risk scores and test their associations to incident diseases.

RESULTS

PXS was associated with higher levels of clinical risk factors including BMI, systolic blood pressure, blood glucose, triglycerides, and HbA1c in individuals without overt or diagnosed T2D. In addition to predicting incident T2D, PXS and PGS were significantly and positively associated with the incidence of all 7 other chronic diseases. There were 4% and 8% of individuals in the bottom deciles of PXS and PGS, respectively, who were prediabetic at baseline but had low risks of T2D and other chronic diseases. Compared to the remaining population, individuals in the top deciles of PGS and PXS had particularly high risks of developing chronic diseases. For instance, the hazard ratio of COPD and obesity for individuals in the top T2D PXS deciles was 2.82 (95% CI 2.39-3.35, P = 4.00 × 10-33) and 2.54 (95% CI 2.24-2.87, P = 9.86 × 10-50), respectively, compared to the remaining population. We also found that PXS and PGS were both significantly (P < 0.0001) and positively associated with the total number of incident diseases.

CONCLUSIONS

T2D shares polyexposure risks with other common chronic diseases. Individuals with an elevated genetic and non-genetic risk of T2D also have high risks of cardiovascular, liver, lung, and kidney diseases.

Identification of genetic loci simultaneously associated with multiple cardiometabolic traits

BACKGROUND AND AIMS

Cardiometabolic disorders (CMD) arise from a constellation of features such as increased adiposity, hyperlipidemia, hypertension and compromised glucose control. Many genetic loci have shown associations with individual CMD-related traits, but no investigations have focused on simultaneously identifying loci showing associations across all domains. We therefore sought to identify loci associated with risk across seven continuous CMD-related traits.

METHODS AND RESULTS

We conducted separate genome-wide association studies (GWAS) for systolic and diastolic blood pressure (SBP/DBP), hemoglobin A1c (HbA1c), low- and high- density lipoprotein cholesterol (LDL-C/HDL-C), waist-to-hip-ratio (WHR), and triglycerides (TGs) in the UK Biobank (N = 356,574-456,823). Multiple loci reached genome-wide levels of significance (N = 145-333) for each trait, but only four loci (in/near VEGFA, GRB14-COBLL1, KLF14, and RGS19-OPRL1) were associated with risk across all seven traits (P < 5 × 10^-8). We sought replication of these four loci in an independent set of seven trait-specific GWAS meta-analyses. GRB14-COBLL1 showed the most consistent replication, revealing nominally significant associations (P < 0.05) with all traits except DBP.

CONCLUSIONS

Our analyses suggest that very few loci are associated in the same direction of risk with traits representing the full spectrum of CMD features. We identified four such loci, and an understanding of the pathways between these loci and CMD risk may eventually identify factors that can be used to identify pathologic disturbances that represent broadly beneficial therapeutic targets.

Gene-based association tests using GWAS summary statistics and incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.

Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank

Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.

Multi-Trait Genome-Wide Association Study of Atherosclerosis Detects Novel Pleiotropic Loci

Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic (CAD, PAD) and atherosclerosis risk factors (body mass index, smoking initiation, type 2 diabetes, low density lipoprotein, high density lipoprotein, total cholesterol, and triglycerides), we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies.

Emerging Insights on the Diverse Roles of Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) in Chronic Liver Diseases: Cholesterol Metabolism and Beyond

Chronic liver diseases are commonly associated with dysregulated cholesterol metabolism. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a serine protease of the proprotein convertase family that is mainly synthetized and secreted by the liver, and represents one of the key regulators of circulating low-density lipoprotein (LDL) cholesterol levels. Its ability to bind and induce LDL-receptor degradation, in particular in the liver, increases circulating LDL-cholesterol levels in the blood. Hence, inhibition of PCSK9 has become a very potent tool for the treatment of hypercholesterolemia. Besides PCSK9 limiting entry of LDL-derived cholesterol, affecting multiple cholesterol-related functions in cells, more recent studies have associated PCSK9 with various other cellular processes, including inflammation, fatty acid metabolism, cancerogenesis and visceral adiposity. It is increasingly becoming evident that additional roles for PCSK9 beyond cholesterol homeostasis are crucial for liver physiology in health and disease, often contributing to pathophysiology. This review will summarize studies analyzing circulating and hepatic PCSK9 levels in patients with chronic liver diseases. The factors affecting PCSK9 levels in the circulation and in hepatocytes, clinically relevant studies and the pathophysiological role of PCSK9 in chronic liver injury are discussed.

Biochemical risk factors of atherosclerotic cardiovascular disease: from a narrow and controversial approach to an integral approach and precision medicine

INTRODUCTION

Guidelines of management of dyslipidemias and prevention of cardiovascular disease (CVD) are based on firm scientific evidence obtained by randomized controlled trials (RCTs). However, the role of elevated low-density lipoprotein-cholesterol (LDL-C)as a risk factor of CVD and therapies to lower LDL-C are frequently disputed by colleagues who disagree with the conclusions of the RCTs published. This review focuses on this dispute, and evaluates the current approach of management of dyslipidemias and CVD prevention to find modern alternatives for more precise diagnosis and therapy of dyslipidemic patients.

AREAS COVERED

Recent interest in lipoprotein(a) (Lp(a)) and remnants lipoproteins and in therapies that do not influence LDL-C levels primarily, such as anti-inflammatory drugs and icosapent ethyl, has revitalized our concern to optimize the care for patients with increased CVD risk without focusing simply on reduction of LDL-C by therapy with statins, ezitemibe, and proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors.

EXPERT OPINION

The limited characterization of study populations by measurement of total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C) and triglycerides (TG) followed by measurement or calculation of LDL-C should be extended by a more integral approach in order to realize precision diagnostics and precision medicine, for the sake of personalized patient care.

Association of metabolic syndrome and the risk of bladder cancer: A prospective cohort study

Background

Metabolic syndrome (MetS) and its components have been shown as risk factors for several solid cancers. However, current epidemiological evidence about the relevance of MetS and bladder cancer risk was limited.

Methods

We conducted a prospective cohort study of 476,986 participants with undiagnosed bladder cancer based on the UK Biobank. MetS was defined as the presence of at least three of the five selected indicators: hypertension, central obesity, raised triglyceride, reduced HDL-cholesterol, and raised fasting plasma glucose. Bladder cancer has been identified through contact with the British Cancer Registry (median follow-up time: 6.6 years). We assessed hazard ratio (HR) and 95% confidence interval (CI) through Cox proportional hazard regression after adjusting for demographic and lifestyle factors. Non-linear associations for individual MetS components were assessed by the restricted cubic spline method.

Results

During a follow-up of 3,112,566 person-years, 487 cases of bladder cancer were ascertained. MetS (HR = 1.32, 95% CI = 1.08-1.61), central obesity (HR = 1.39, 95% CI = 1.15-1.68), dyslipidemia for HDL cholesterol (HR = 1.31, 95% CI = 1.04-1.66), and hyperglycemia (HR = 1.44, 95% CI = 1.16-1.79) were associated with elevated risk of bladder cancer. Bladder cancer risk increased with the number of MetS components. In stratified analyses, MetS showed similar effects in bladder cancer independently with sex, age, cigarette and alcohol use, physical activity, and dietary factors. Higher waist circumference, BMI, fasting blood glucose, and glycosylated hemoglobin were independently associated with increased risk of bladder cancer, with no evidence against non-linearity.

Conclusion

MetS might be an independent risk factor for bladder cancer. Our findings highlighted the importance of individualized management of MetS components for preventing bladder cancer.

Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes

The majority of risk loci identified by genome-wide association studies (GWAS) are in non-coding regions, hampering their functional interpretation. Instead, transcriptome-wide association studies (TWAS) identify gene-trait associations, which can be used to prioritize candidate genes in disease-relevant tissue(s). Here, we aimed to systematically identify susceptibility genes for coronary artery disease (CAD) by TWAS. We trained prediction models of nine CAD-relevant tissues using EpiXcan based on two genetics-of-gene-expression panels, the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) and the Genotype-Tissue Expression (GTEx). Based on these prediction models, we imputed gene expression of respective tissues from individual-level genotype data on 37,997 CAD cases and 42,854 controls for the subsequent gene-trait association analysis. Transcriptome-wide significant association (i.e. P < 3.85e-6) was observed for 114 genes. Of these, 96 resided within previously identified GWAS risk loci and 18 were novel. Stepwise analyses were performed to study their plausibility, biological function, and pathogenicity in CAD, including analyses for colocalization, damaging mutations, pathway enrichment, phenome-wide associations with human data and expression-traits correlations using mouse data. Finally, CRISPR/Cas9-based gene knockdown of two newly identified TWAS genes, RGS19 and KPTN, in a human hepatocyte cell line resulted in reduced secretion of APOB100 and lipids in the cell culture medium. Our CAD TWAS work (i) prioritized candidate causal genes at known GWAS loci, (ii) identified 18 novel genes to be associated with CAD, and iii) suggested potential tissues and pathways of action for these TWAS CAD genes.

Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol

OBJECTIVE

LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown.

RESEARCH DESIGN AND METHODS

We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses.

RESULTS

A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (β = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04).

CONCLUSIONS

These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

Linear and nonlinear analyses of the association between low-density lipoprotein cholesterol and diabetes: The spurious U-curve in observational study

OBJECTIVE

Hyperlipidemia is traditionally considered a risk factor for diabetes. The effect of low-density lipoprotein cholesterol (LDL-C) is counterintuitive to diabetes. We sought to investigate the relationship between LDL-C and diabetes for better lipid management.

METHODS

We tested the shape of association between LDL-C and diabetes and created polygenic risk scores of LDL-C and generated linear Mendelian randomization (MR) estimates for the effect of LDL-C and diabetes. We evaluated for nonlinearity in the observational and genetic relationship between LDL-C and diabetes.

RESULTS

Traditional observational analysis suggested a complex non-linear association between LDL-C and diabetes while nonlinear MR analyses found no evidence for a non-linear association. Under the assumption of linear association, we found a consistently protective effect of LDL-C against diabetes among the females without lipid-lowering drugs use. The ORs were 0.84 (95% CI, 0.72-0.97, P=0.0168) in an observational analysis which was more prominent in MR analysis and suggested increasing the overall distribution of LDL-C in females led to an overall decrease in the risk of diabetes (P=0.0258).

CONCLUSIONS

We verified the liner protective effect of LDL-C against diabetes among the females without lipid-lowering drug use. Non-linear associations between LDL-C against diabetes in observational analysis are not causal.

Genetic Determinants of Plasma Low-Density Lipoprotein Cholesterol Levels: Monogenicity, Polygenicity, and "Missing" Heritability

Changes in plasma low-density lipoprotein cholesterol (LDL-c) levels relate to a high risk of developing some common and complex diseases. LDL-c, as a quantitative trait, is multifactorial and depends on both genetic and environmental factors. In the pregenomic age, targeted genes were used to detect genetic factors in both hyper- and hypolipidemias, but this approach only explained extreme cases in the population distribution. Subsequently, the genetic basis of the less severe and most common dyslipidemias remained unknown. In the genomic age, performing whole-exome sequencing in families with extreme plasma LDL-c values identified some new candidate genes, but it is unlikely that such genes can explain the majority of inexplicable cases. Genome-wide association studies (GWASs) have identified several single-nucleotide variants (SNVs) associated with plasma LDL-c, introducing the idea of a polygenic origin. Polygenic risk scores (PRSs), including LDL-c-raising alleles, were developed to measure the contribution of the accumulation of small-effect variants to plasma LDL-c. This paper discusses other possibilities for unexplained dyslipidemias associated with LDL-c, such as mosaicism, maternal effect, and induced epigenetic changes. Future studies should consider gene-gene and gene-environment interactions and the development of integrated information about disease-driving networks, including phenotypes, genotypes, transcription, proteins, metabolites, and epigenetics.

Gut microbiome signatures distinguish type 2 diabetes mellitus from non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is closely associated with type 2 diabetes mellitus (T2D), and these two metabolic diseases demonstrate bidirectional influences. The identification of microbiome profiles that are specific to liver injury or impaired glucose metabolism may assist understanding of the role of the gut microbiota in the relationship between NAFLD and T2D. Here, we studied a biopsy-proven Asian NAFLD cohort (n = 329; 187 participants with NAFLD, 101 with NAFLD and T2D, and 41 with neither) and identified Enterobacter, Romboutsia, and Clostridium sensu stricto as the principal taxa associated with the severity of NAFLD and T2D, whereas Ruminococcus and Megamonas were specific to NAFLD. In particular, the taxa that were associated with both severe liver pathology and T2D were also significantly associated with markers of diabetes, such as fasting blood glucose and Hb1Ac. Enterotype analysis demonstrated that participants with NAFLD had a significantly higher proportion of Bacteroides and a lower proportion of Ruminococcus than a Korean healthy twin cohort (n = 756). However, T2D could not be clearly distinguished from NAFLD. Analysis of an independent T2D cohort (n = 185) permitted us to validate the T2D-specific bacterial signature identified in the NAFLD cohort. Functional inference analysis revealed that endotoxin biosynthesis pathways were significantly enriched in participants with NAFLD and T2D, compared with those with NAFLD alone. These findings may assist with the development of effective therapeutic approaches for metabolic diseases that are associated with specific bacterial signatures.