A novel regulatory facet for hypertriglyceridemia: The role of microRNAs in the regulation of triglyceride-rich lipoprotein biosynthesis

Atherosclerotic cardiovascular disease (ASCVD) is one of the major leading global causes of death. Genetic and epidemiological studies strongly support the causal association between triacylglycerol-rich lipoproteins (TAGRL) and atherogenesis, even in statin-treated patients. Recent genetic evidence has clarified that variants in several key genes implicated in TAGRL metabolism are strongly linked to the increased ASCVD risk. There are several triacylglycerol-lowering agents; however, new therapeutic options are in development, among which are miRNA-based therapeutic approaches. MicroRNAs (miRNAs) are small non-coding RNAs (18–25 nucleotides) that negatively modulate gene expression through translational repression or degradation of target mRNAs, thereby reducing the levels of functional genes. MiRNAs play a crucial role in the development of hypertriglyceridemia as several miRNAs are dysregulated in both synthesis and clearance of TAGRL particles. MiRNA-based therapies in ASCVD have not yet been applied in human trials but are attractive. This review provides a concise overview of current interventions for hypertriglyceridemia and the development of novel miRNA and siRNA-based drugs. We summarize the miRNAs involved in the regulation of key genes in the TAGRLs synthesis pathway, which has gained attention as a novel target for therapeutic applications in CVD.     

Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization

Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual’s lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual’s lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.     

New Opportunities of Endovascular Recanalization Techniques in the Management of Ischemic Stroke

Currently, there are a limited number of treatments for ischemic stroke. Intravenous thrombolytic therapy is the most effective method. However, its efficacy is very low in the case of stroke caused by occlusion of large intracranial arteries. Previous studies of endovascular recanalization techniques in the treatment of stroke, particularly randomized controlled trials (RCTs), such as IMS-3, SYNTHESIS Expansion, and MR RESCUE, have shown negative results. The results of other RCTs (MR CLEAN, ESCAPE, EXTENDIA, SWIFT PRIME, and REVASCAT) published in 2015 demonstrated that, in the case of careful selection of patients with verified occlusion of large intracranial arteries, endovascular recanalization significantly improves stroke outcomes.     

Genes influencing HDL metabolism: new perspectives and implications for atherosclerosis prevention

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of morbidity and mortality in Western societies. Current therapies, such as reduction of plasma cholesterol, significantly reduce, but do not come close to eliminating, the complications of ASCVD. Therefore, novel therapeutic approaches to the prevention of acute coronary events and progression of atherosclerosis are still needed. The complex metabolism of high density lipoproteins represents an attractive potential target for therapeutic intervention. Here, we will discuss those components of the high density lipoprotein metabolism and lipid transport pathways that are potential preventative or therapeutic targets for ASCVD.     

Genetic Insights into Cardiometabolic Risk Factors

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as ‘cardiometabolic’ risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification.     

HDL cholesterol concentrations and risk of atherosclerotic cardiovascular disease – Insights from randomized clinical trials and human genetics

Through seven decades the inverse association between HDL cholesterol concentrations and risk of atherosclerotic cardiovascular disease (ASCVD) has been observed in case-control and prospective cohort studies. This robust inverse association fuelled the enthusiasm towards development of HDL cholesterol increasing drugs, exemplified by the cholesteryl ester transfer protein (CETP) inhibitor trials and the extended-release niacin HPS2-THRIVE trial. These HDL cholesterol increasing trials were launched without conclusive evidence from human genetics, and despite discrepant species dependent evidence from animal studies. Evidence from human genetics and from randomized clinical trials over the last 13 years now point in the direction that concentrations of HDL cholesterol, do not appear to be a viable future path to target therapeutically for prevention of ASCVD. A likely explanation for the strong observational association between low HDL cholesterol and high ASCVD risk is the concomitant inverse association between HDL cholesterol and atherogenic triglyceride-rich lipoproteins. The purpose of the present review is to bring HDL cholesterol increasing trials into a human genetics context exemplified by candidate gene studies of key players in HDL biogenesis as well as by HDL cholesterol related genome-wide association studies.     

Association between genetically determined telomere length and health-related outcomes: A systematic review and meta-analysis of Mendelian randomization studies

Emerging evidence has shown that leukocyte telomere length (LTL) is associated with various health-related outcomes, while the causality of these associations remains unclear. We performed a systematic review and meta-analysis of current evidence from Mendelian randomization (MR) studies on the association between LTL and health-related outcomes. We searched PubMed, Embase, and Web of Science up to April 2022 to identify eligible MR studies. We graded the evidence level of each MR association based on the results of the main analysis and four sensitive MR methods, MR-Egger, weighted median, MR-PRESSO, and multivariate MR. Meta-analyses of published MR studies were also performed. A total of 62 studies with 310 outcomes and 396 MR associations were included. Robust evidence level was observed for the association between longer LTL and increased risk of 24 neoplasms (the strongest magnitude for osteosarcoma, GBM, glioma, thyroid cancer, and non-GBM glioma), six genitourinary and digestive system outcomes of excessive or abnormal growth, hypertension, metabolic syndrome, multiple sclerosis, and clonal hematopoiesis of indeterminate potential. Robust inverse association was observed for coronary heart disease, chronic kidney disease, rheumatoid arthritis, juvenile idiopathic arthritis, idiopathic pulmonary fibrosis, and facial aging. Meta-analyses of MR studies suggested that genetically determined LTL was associated with 12 neoplasms and 9 nonneoplasm outcomes. Evidence from published MR studies supports that LTL plays a causal role in various neoplastic and nonneoplastic diseases. Further research is required to elucidate the underlying mechanisms and to bring insight into the potential prediction, prevention, and therapeutic applications of telomere length.     

Using genetic variation to disentangle the complex relationship between food intake and health outcomes

Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes.     

Causal association between heart failure and bone mineral density: Insights from a two-sample bidirectional Mendelian randomization study

In recent times, the association between HF and BMD has attracted enormous interest in the scientific community. However, published epidemiological observational studies on the relationship between HF and BMD remain inconclusive. Herein, we evaluated from the analytical perspective a two-sample bidirectional MR study to analyze the causal association between HF and BMD using a summary-level GWAS Catalog. To select instrumental SNPs strongly associated with exposure, we took a series of rigorous quality control steps at the time of analysis. The causal MR assessment of HF on the risk of BMD was performed first and then in the opposite direction. To make the conclusions more reliable and robust, the fixed-effects IVW, weighted median-based method, MR–Egger, simple mode and weighted mode were utilized. A maximum likelihood model was also used if necessary. MR–Egger regression, IVW “leave-one-out” sensitivity analysis, MR-PRESSO, MR–Egger intercept test and Cochran's Q statistic methods were used to assess heterogeneity and pleiotropy. Our MR studies supported a meaningful causal association between HF and TB-BMD (IVW: OR = 0.78, 95% CI: 0.68–0.87, p = 0.00588). At the same time, we did not find a significant causal relationship between HF and FA-BMD, FN-BMD or LS-BMD. No significant causal relationships between BMD and HF were observed. This bidirectional MR analysis suggested a causal association of HF with only low TB-BMD, while the reverse causality hypothesis was not found. Studies of the prevention and treatment of total bone mineral density decline in patients with heart failure need to be performed.     

Effect of Vitamin D3 Supplementation on Inflammatory Markers and Glycemic Measures among Overweight or Obese Adults: A Systematic Review of Randomized Controlled Trials

BackgroundObesity induced low-grade chronic inflammation disrupts proper immune and metabolic function. Vitamin D deficiency increases inflammation, which is associated with cardiometabolic risk. This systematic review examines the association between oral vitamin D (VD) supplementation and circulating inflammatory biomarkers and glycemic outcomes from randomized controlled trials (RCTs) of overweight and/or obese adults.MethodsMEDLINE OVID, EMBASE and the Cochrane Central Register of Controlled Trials were searched according to a predefined protocol. Eligible RCTs included adults randomized to receive either oral VD or placebo. Two reviewers independently assessed RCTs for inclusion. Bias was assessed using the Cochrane Collaboration risk of bias tool. Mean differences were calculated comparing end-of-study sample means between the independent VD and placebo groups.ResultsEleven unique RCTs met inclusion criteria from a total of 3,383 identified citations, including 79 screened articles and 14 full text data extractions. Inflammatory and glycemic measures were reported in 7 and 10 RCTs, respectively. Most trial findings were non-significant with considerable heterogeneity in design, participants and outcomes. All but one trial was rated as either high or unclear risk of bias. Two RCTs reported significant changes in inflammatory biomarkers; however, the mean difference between groups was not statistically significant: C-reactive protein 0.19 mg/L (p = 0.88); Tumor Necrosis Factor -0.54 pg/ml (p = 0.20). Two other trials found significant mean differences in fasting plasma glucose -0.32 mmol/L (p = 0.03), Hemoglobin A1c -0.13% (p = 0.04), and Homeostatic Model Assessment -0.86 (p = 0.02) following VD supplementation.ConclusionsOverall, there is no clear established benefit of VD supplementation on inflammatory biomarkers among overweight/obese adults. Baseline serum VD possibly influences the effect of VD repletion on inflammatory markers. Risk of bias was present in most studies, thus supporting the need for higher quality studies in this area to more conclusively understand the role VD supplementation has on inflammatory pathways.     

Glycation and biomarkers of vascular complications of diabetes

Propensity to diabetic nephropathy (DN), retinopathy (DR), and cardiovascular disease (CVD) varies between individuals. Current biomarkers such as indicators of glycemia (HbA1c), retinal examinations, and albuminuria, cannot detect early tissue damage. HbAIc also doesn’t reflect most glycative and oxidative chemical pathways that cause complications, and studies of new biomarkers to measure their end-products are needed. This review proposes the study of advanced glycation end products (AGEs) and oxidation end-products (OPs) in long-term diabetes outcome studies. AGEs integrate the activity of glycation pathways that form dicarbonyls, while OPs reflect superoxides, hydroxyl radicals, and peroxides. We discuss using these biomarkers to predict risk of development and progression of DN, DR, and CVD, and to determine if they confer risk independently of the level of HbA1c. We also discuss methods and guidelines to document sample quality in such studies. These studies have the potential to validate unique biomarkers during the early stages of diabetes in those who are at high risk of diabetic complications. Information on basic mechanisms responsible for complications could also stimulate development of therapeutic approaches to delay or arrest them. The ultimate goal is to predict those requiring aggressive therapies during the earliest stages, when prevention or reversal of complications is still possible.     

Vitamin D levels and risk of type 1 diabetes: A Mendelian randomization study

BackgroundVitamin D deficiency has been associated with type 1 diabetes in observational studies, but evidence from randomized controlled trials (RCTs) is lacking. The aim of this study was to test whether genetically decreased vitamin D levels are causally associated with type 1 diabetes using Mendelian randomization (MR).Methods and findingsFor our two-sample MR study, we selected as instruments single nucleotide polymorphisms (SNPs) that are strongly associated with 25-hydroxyvitamin D (25OHD) levels in a large vitamin D genome-wide association study (GWAS) on 443,734 Europeans and obtained their corresponding effect estimates on type 1 diabetes risk from a large meta-analysis of 12 type 1 diabetes GWAS studies (Ntot = 24,063, 9,358 cases, and 15,705 controls). In addition to the main analysis using inverse variance weighted MR, we applied 3 additional methods to control for pleiotropy (MR-Egger, weighted median, and mode-based estimate) and compared the respective MR estimates. We also undertook sensitivity analyses excluding SNPs with potential pleiotropic effects. We identified 69 lead independent common SNPs to be genome-wide significant for 25OHD, explaining 3.1% of the variance in 25OHD levels. MR analyses suggested that a 1 standard deviation (SD) decrease in standardized natural log-transformed 25OHD (corresponding to a 29-nmol/l change in 25OHD levels in vitamin D–insufficient individuals) was not associated with an increase in type 1 diabetes risk (inverse-variance weighted (IVW) MR odds ratio (OR) = 1.09, 95% CI: 0.86 to 1.40, p = 0.48). We obtained similar results using the 3 pleiotropy robust MR methods and in sensitivity analyses excluding SNPs associated with serum lipid levels, body composition, blood traits, and type 2 diabetes. Our findings indicate that decreased vitamin D levels did not have a substantial impact on risk of type 1 diabetes in the populations studied. Study limitations include an inability to exclude the existence of smaller associations and a lack of evidence from non-European populations.ConclusionsOur findings suggest that 25OHD levels are unlikely to have a large effect on risk of type 1 diabetes, but larger MR studies or RCTs are needed to investigate small effects.

Despoina Manousaki and co-workers investigate vitamin D levels and risk of type I diabetes.     

Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that “all genes affect every complex trait” complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.     

Approximation of bias and mean-squared error in two-sample Mendelian randomization analyses

Mendelian randomization (MR) is a type of instrumental variable (IV) analysis that uses genetic variants as IVs for a risk factor to study its causal effect on an outcome. Extensive investigations on the performance of IV analysis procedures, such as the one based on the two-stage least squares (2SLS) procedure, have been conducted under the one-sample scenario, where measures on IVs, the risk factor, and the outcome are assumed to be available for each study participant. Recent MR analysis usually is performed with data from two independent or partially overlapping genetic association studies (two-sample setting), with one providing information on the association between the IVs and the outcome, and the other on the association between the IVs and the risk factor. We investigate the performance of 2SLS in the two-sample–based MR when the IVs are weakly associated with the risk factor. We derive closed form formulas for the bias and mean squared error of the 2SLS estimate and verify them with numeric simulations under realistic circumstances. Using these analytic formulas, we can study the pros and cons of conducting MR analysis under one-sample and two-sample settings and assess the impact of having overlapping samples. We also propose and validate a bias-corrected estimator for the causal effect.     

Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference

Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.     

Combining environmental exposure and genetic effect measurements in health outcome assessment.

The presence of overwhelming difficulties in assessing the extent or even the presence of a causal association between modern environmental exposures and disease has promoted the use of more complex models in the design of human biomonitoring studies. The concatenation of environmental exposure, genetic effect and individual susceptibility is a key issue in the assessment of risks for populations exposed to environmental pollutants. The use of a biological event laying in the causal pathway from exposure to outcome as surrogate end-point of disease, can potentially anticipate clinical diagnosis, offering a number of possibilities for application of preventive measures. Numerous biomarkers are currently employed to study human populations exposed to environmental carcinogens, among these, the frequency of chromosomal aberration (CA) in peripheral blood lymphocytes has the most abundant literature linking a genetic effect with the occurrence of cancer. Findings from recent epidemiological studies which have followed-up a large group of healthy subjects screened for CAs have lent further support to the use of chromosomal breakage as a relevant biomarker of cancer risk. The applicability of surrogate end-points of cancer on an individual basis thus far seems to be limited to few examples. On the other hand, from a public health outlook, increases in the frequency of surrogate end-points are suggestive of an increased risk of cancer, and for validated biomarkers such as CAs intervention policies and actions in exposed populations showing increased frequency of these end-points should be always recommended.     

Pesticides and cancer: insights into toxicoproteomic-based findings

Humans are often exposed to a variety of pollutants that contribute to an individual's risk for diseases including cancer. Animal, cell cultures and epidemiological lines of evidence demonstrate that exposure to various environmental pollutants including pesticides are associated with increasing frequency of cancers. Organophosphates, organochlorines, carbamates, pyrethroids, the major groups of pesticides, have been reported to be carcinogenic in various models. However, the results of these studies are still controversial, nevertheless, their mechanism of action is clear. Therefore, new strategies in toxicological research are needed for efficient screening for adverse effects of pesticides on complex living systems. Biomarkers can be employed to identify causal associations and to make better quantitative and qualitative estimates of those associations at relevant levels of exposure. This will enable us to deepen our understanding of mechanism behind their carcinogenic potential. Deciphering the associations between pesticide exposure and cancer, following toxicoproteomics application, will be useful in the development of potential predictive biomarkers of pesticide induced carcinogenicity. Therefore, the thrust of this article was to review the risk of cancer due to pesticide exposure and significant toxicoproteomic-based studies conducted so far, to identify the novel molecules as possible biomarkers for cancer following pesticide exposure.     

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.