Statin Use Associates With Risk of Type 2 Diabetes via Epigenetic Patterns at ABCG1

Longitudinal Association of DNA Methylation With Type 2 Diabetes and Glycemic Traits: A 5-Year Cross-Lagged Twin Study

Investigators of previous cross-sectional epigenome-wide association studies (EWAS) in adults have reported hundreds of 5'-cytosine-phosphate-guanine-3' (CpG) sites associated with type 2 diabetes mellitus (T2DM) and glycemic traits. However, the results from EWAS have been inconsistent, and longitudinal observations of these associations are scarce. Furthermore, few studies have investigated whether DNA methylation (DNAm) could be modified by smoking, drinking, and glycemic traits, which have broad impacts on genome-wide DNAm and result in altering the risk of T2DM. Twin studies provide a valuable tool for epigenetic studies, as twins are naturally matched for genetic information. In this study, we conducted a systematic literature search in PubMed and Embase for EWAS, and 214, 33, and 117 candidate CpG sites were selected for T2DM, HbA1c, and fasting blood glucose (FBG). Based on 1,070 twins from the Chinese National Twin Registry, 67, 17, and 16 CpG sites from previous studies were validated for T2DM, HbA1c, and FBG. Longitudinal review and blood sampling for phenotypic information and DNAm were conducted twice in 2013 and 2018 for 308 twins. A cross-lagged analysis was performed to examine the temporal relationship between DNAm and T2DM or glycemic traits in the longitudinal data. A total of 11 significant paths from T2DM to subsequent DNAm and 15 paths from DNAm to subsequent T2DM were detected, suggesting both directions of associations. For glycemic traits, we detected 17 cross-lagged associations from baseline glycemic traits to subsequent DNAm, and none were from the other cross-lagged direction, indicating that CpG sites may be the consequences, not the causes, of glycemic traits. Finally, a longitudinal mediation analysis was performed to explore the mediation effects of DNAm on the associations of smoking, drinking, and glycemic traits with T2DM. No significant mediations of DNAm in the associations linking smoking and drinking with T2DM were found. In contrast, our study suggested a potential role of DNAm of cg19693031, cg00574958, and cg04816311 in mediating the effect of altered glycemic traits on T2DM.

Do statins benefit low-risk population for primary prevention of atherosclerotic cardiovascular disease: A retrospective cohort study

The reported beneficial effects of statins on cardiovascular outcome based on risk assessment are inconsistent. Therefore, we investigated statin therapy effectiveness for the primary prevention of cardiovascular disease (CVD), according to the Korean Risk Prediction Model (KRPM). Subjects aged 40–79 years with low density lipoprotein cholesterol (LDL-C) of < 190 mg/dL and without CVD history were categorized as statin users or non-users using the National Health Insurance Service-National Sample Cohort (NHIS-NSC) database, Korea, 2002–2015. The 10-year atherosclerotic CVD (ASCVD) risk was calculated using the validated KRPM and categorized as low, borderline, intermediate, or high-risk groups; the incidence of major adverse cardiovascular events (MACEs) was compared over a mean follow-up period of 5.7 years using Cox proportional hazard models. The MACE incidence risk was decreased in statin users [hazard ratio (HR) 0.90, 95% confidence interval (CI) (0.84–0.98)]. However, there was an increased risk of MACE incidence in low-risk statin users [HR 1.80, 95% CI (1.29–2.52)], and no significant relationship was identified between statin use and MACE in the borderline [HR 1.15, 95% CI (0.86–1.54)] and intermediate-risk [HR 0.94, 95% CI (0.85–1.03)] groups. The risk of MACE incidence decreased only in the high CVD risk group among statin users. Statin use is not associated with MACE reduction in low- to intermediate-risk participants. Therefore, individuals with LDL-C level of < 190 mg/dL and low ASCVD risk should consider statin therapy only when CVD risk is proved obvious using an appropriate ASCVD risk tool.

Quantification of Biocatalytic Transformations by Single Microbial Cells Enabled by Tailored Integration of Droplet Microfluidics and Mass Spectrometry

Improving the performance of chemical transformations catalysed by microbial biocatalysts requires a deep understanding of cellular processes. While the cellular heterogeneity of cellular characteristics, such as the concentration of high abundant cellular content, is well studied, little is known about the reactivity of individual cells and its impact on the chemical identity, quantity, and purity of excreted products. Biocatalytic transformations were monitored chemically specific and quantifiable at the single-cell level by integrating droplet microfluidics, cell imaging, and mass spectrometry. Product formation rates for individual Saccharomyces cerevisiae cells were obtained by i) incubating nanolitre-sized droplets for product accumulation in microfluidic devices, ii) an imaging setup to determine the number of cells in the droplets, and iii) electrospray ionisation mass spectrometry for reading the chemical contents of individual droplets. These findings now enable the study of whole-cell biocatalysis at single-cell resolution.

Epigenetics of type 2 diabetes mellitus and weight change - a tool for precision medicine?

Pioneering studies performed over the past few decades demonstrate links between epigenetics and type 2 diabetes mellitus (T2DM), the metabolic disorder with the most rapidly increasing prevalence in the world. Importantly, these studies identified epigenetic modifications, including altered DNA methylation, in pancreatic islets, adipose tissue, skeletal muscle and the liver from individuals with T2DM. As non-genetic factors that affect the risk of T2DM, such as obesity, unhealthy diet, physical inactivity, ageing and the intrauterine environment, have been associated with epigenetic modifications in healthy individuals, epigenetics probably also contributes to T2DM development. In addition, genetic factors associated with T2DM and obesity affect the epigenome in human tissues. Notably, causal mediation analyses found DNA methylation to be a potential mediator of genetic associations with metabolic traits and disease. In the past few years, translational studies have identified blood-based epigenetic markers that might be further developed and used for precision medicine to help patients with T2DM receive optimal therapy and to identify patients at risk of complications. This Review focuses on epigenetic mechanisms in the development of T2DM and the regulation of body weight in humans, with a special focus on precision medicine.

Using Mendelian randomisation to identify opportunities for type 2 diabetes prevention by repurposing medications used for lipid management

BACKGROUND

Maintaining a healthy lifestyle to reduce type 2 diabetes (T2D) risk is challenging and additional strategies for T2D prevention are needed. We evaluated several lipid control medications as potential therapeutic options for T2D prevention using tissue-specific predicted gene expression summary statistics in a two-sample Mendelian randomisation (MR) design.

METHODS

Large-scale European genome-wide summary statistics for lipids and T2D were leveraged in our multi-stage analysis to estimate changes in either lipid levels or T2D risk driven by tissue-specific predicted gene expression. We incorporated tissue-specific predicted gene expression summary statistics to proxy therapeutic effects of three lipid control medications [i.e., statins, icosapent ethyl (IPE), and proprotein convertase subtilisin/kexin type-9 inhibitors (PCSK-9i)] on T2D susceptibility using two-sample Mendelian randomisation (MR).

FINDINGS

IPE, as proxied via increased FADS1 expression, was predicted to lower triglycerides and was associated with a 53% reduced risk of T2D. Statins and PCSK-9i, as proxied by reduced HMGCR and PCSK9 expression, respectively, were predicted to lower LDL-C levels but were not associated with T2D susceptibility.

INTERPRETATION

Triglyceride lowering via IPE may reduce the risk of developing T2D in populations of European ancestry. However, experimental validation using animal models is needed to substantiate our results and to motivate randomized control trials (RCTs) for IPE as putative treatment for T2D prevention.

FUNDING

Only summary statistics were used in this analysis. Funding information is detailed under Acknowledgments.

Association of DNA methylation and genetic variations of the APOE gene with the risk of diabetic dyslipidemia

Apolipoprotein E (APOE) is a key regulator of lipoprotein metabolism, and consequently, affects the plasma and tissue lipid contents. The aim of the present study was to investigate the parallel effects of APOE genetic variants and promoter methylation levels of six CpGs on the risk of diabetic dyslipidemia. A total of 204 Palestinian type 2 diabetes (T2D) patients (mean age ± SD: 62.7±10.2) were enrolled in the present study (n=96 with dyslipidemia and n=108 without dyslipidemia). Next generation sequencing was performed to analyze five single nucleotide polymorphisms: Two variants rs7412 and rs429358 that determine APOE ε alleles, and three variants in the promoter region (rs769446, rs449647, and rs405509). For all subjects, the most common genotype was ε3/ε3 (79.4%). No statistical differences were observed in the APOE ε polymorphisms and the three promoter variants among T2D patients with and without dyslipidemia (P>0.05). A comparison of lipid parameters between ε3/ε3 subjects and ε4 carriers in both groups revealed no significant differences in the mean values of LDL-C, HDL-C, TG, and TC levels (P>0.05). Six CpG sites in the APOE promoter on chromosome 19:44905755-44906078 were identified, and differential DNA methylation in these CpGs were observed between the study groups. Logistic regression analysis revealed a significant association of DNA methylation level at the six CpGs with an increased risk of diabetic dyslipidemia (odds ratio, 1.038; 95% confidence interval, 1.012-1.064; P=0.004). In conclusion, the present study revealed that DNA methylation levels in six CpGs in the APOE promoter region was associated with the risk of diabetic dyslipidemia independently of the APOE ε4 variant which could be a potential therapeutic target to reverse the methylation of the APOE promoter.

Impact of DNA methylation on ADME gene expression, drug disposition and efficacy

Interindividual differences in drug response have always existed in clinical treatment. Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) play an important role in the process of pharmacokinetics. The effects of genetic polymorphism and nuclear receptors on the expression of drug metabolism enzymes and transporters can only explain some individual differences in clinical treatment. Several key ADME genes have been demonstrated to be regulated by epigenetic mechanisms that can potentially affect interindividual variability in medical treatment. Emerging studies have focused on the importance of DNA methylation for ADME gene expression and for drug response. Among them, the most studied is anti-tumor drugs, and followed by anti-tuberculous and anti-platelet drugs. Therefore, we provide an epigenetics perspective on variability in drug response. The review summarizes the correlation between ADME gene expression and DNA methylation, including the exact methylation locations, and focuses on the corresponding drug disposition and effects to illuminate interindividual differences in clinical medication.

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

Epigenetics and microRNAs in cardiovascular diseases

Cardiovascular diseases are among the leading causes of mortality worldwide. Besides environmental and genetic changes, these disorders can be influenced by processes which do not affect DNA sequence yet still play an important role in gene expression and which can be inherited. These so-called 'epigenetic' changes include DNA methylation, histone modifications, and ATP-dependent chromatin remodeling enzymes, which influence chromatin remodeling and gene expression. Next to these, microRNAs are non-coding RNA molecules that silence genes post-transcriptionally. Both epigenetic factors and microRNAs are known to influence cardiac development and homeostasis, in an individual fashion but also in a complex regulatory network. In this review, we will discuss how epigenetic factors and microRNAs interact with each other and how together they can influence cardiovascular diseases.

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

BACKGROUND

The discovery of robust and trans-ethnically replicated DNA methylation markers of metabolic phenotypes, has hinted at a potential role of epigenetic mechanisms in lipid metabolism. However, DNA methylation and the lipid compositions and lipid concentrations of lipoprotein sizes have been scarcely studied. Here, we present an epigenome-wide association study (EWAS) (N = 5414 total) of mostly lipid-related metabolic measures, including a fine profiling of lipoproteins. As lipoproteins are the main players in the different stages of lipid metabolism, examination of epigenetic markers of detailed lipoprotein features might improve the diagnosis, prognosis, and treatment of metabolic disturbances.

RESULTS

We conducted an EWAS of leukocyte DNA methylation and 226 metabolic measurements determined by nuclear magnetic resonance spectroscopy in the population-based KORA F4 study (N = 1662) and replicated the results in the LOLIPOP, NFBC1966, and YFS cohorts (N = 3752). Follow-up analyses in the discovery cohort included investigations into gene transcripts, metabolic-measure ratios for pathway analysis, and disease endpoints. We identified 161 associations (p value < 4.7 × 10-10), covering 16 CpG sites at 11 loci and 57 metabolic measures. Identified metabolic measures were primarily medium and small lipoproteins, and fatty acids. For apolipoprotein B-containing lipoproteins, the associations mainly involved triglyceride composition and concentrations of cholesterol esters, triglycerides, free cholesterol, and phospholipids. All associations for HDL lipoproteins involved triglyceride measures only. Associated metabolic measure ratios, proxies of enzymatic activity, highlight amino acid, glucose, and lipid pathways as being potentially epigenetically implicated. Five CpG sites in four genes were associated with differential expression of transcripts in blood or adipose tissue. CpG sites in ABCG1 and PHGDH showed associations with metabolic measures, gene transcription, and metabolic measure ratios and were additionally linked to obesity or previous myocardial infarction, extending previously reported observations.

CONCLUSION

Our study provides evidence of a link between DNA methylation and the lipid compositions and lipid concentrations of different lipoprotein size subclasses, thus offering in-depth insights into well-known associations of DNA methylation with total serum lipids. The results support detailed profiling of lipid metabolism to improve the molecular understanding of dyslipidemia and related disease mechanisms.