Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition

Personalized Profiling of Lipoprotein and Lipid Metabolism Based on 1018 Measures from Combined Quantitative NMR and LC-MS/MS Platforms

Applications of advanced omics methodologies are increasingly popular in biomedicine. However, large-scale studies aiming at clinical translation are typically siloed to single technologies. Here, we present the first comprehensive large-scale population data combining 209 lipoprotein measures from a quantitative NMR spectroscopy platform and 809 lipid classes and species from a quantitative LC-MS/MS platform. These data with 1018 molecular measures were analyzed in two population cohorts totaling 7830 participants. The association and cluster analyses revealed excellent coherence between the methodologically independent data domains and confirmed their quantitative compatibility and suitability for large-scale studies. The analyses elucidated the detailed molecular characteristics of the heterogeneous circulatory macromolecular lipid transport system and the underlying structural and compositional relationships. Unsupervised neural network analysis─the so-called self-organizing maps (SOMs)─revealed that these deep molecular and metabolic data are inherently related to key physiological and clinical population characteristics. The data-driven population subgroups uncovered marked differences in the population distribution of multiple cardiometabolic risk factors. These include, e.g., multiple lipoprotein lipids, apolipoprotein B, ceramides, and oxidized lipids. All 79 structurally unique triglyceride species showed similar associations over the entire lipoprotein cascade and indicated systematically increased risk for carotid intima media thickening and other atherosclerosis risk factors, including obesity and inflammation. The metabolic attributes for 27 individual cholesteryl ester species, which formed six distinct clusters, were more intricate with associations both with higher─e.g., CE(16:1)─and lower─e.g., CE(20:4)─cardiometabolic risk. The molecular details provided by these combined data are unprecedented for molecular epidemiology and demonstrate a new potential avenue for population studies.

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data

The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and cardiometabolic risk factors from MARK-AGE data. A cross-sectional observational study was carried out on 1089 subjects (528 men and 561 women), aged 40-75 years old, randomly recruited age- and sex-stratified individuals from the general population. A correlation analysis exploring the relationships between LDLox and relevant biomarkers was undertaken, as well as the development and validation of several machine learning algorithms, for estimating the risk of the combined status of high blood pressure and obesity for the MARK-AGE subjects. The machine learning models yielded Area Under the Receiver Operating Characteristic Curve Score ranging 0.783-0.839 for the internal validation, while the external validation resulted in an Under the Receiver Operating Characteristic Curve Score between 0.648 and 0.787, with the variables based on LDLox reaching significant importance within the obtained predictions. The current study offers novel insights regarding the combined effects of LDL oxidation and other ageing markers on cardiometabolic risk. Future studies might be extended on larger patient cohorts, in order to obtain reproducible clinical assessment models.

Association of NPC1L1 and HMGCR gene polymorphisms with coronary artery calcification in patients with premature triple-vessel coronary disease

BACKGROUND

Coronary artery calcification (CAC) is a highly specific marker of atherosclerosis. Niemann-Pick C1-like 1 (NPC1L1) and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) are the therapeutic targets of ezetimibe and statins, respectively, which are important for the progression of atherosclerosis. However, CAC's genetic susceptibility with above targets is still unknown. We aimed to investigate the association of NPC1L1 and HMGCR gene polymorphisms with CAC in patients with premature triple-vessel disease (PTVD).

METHODS

Four single nucleotide polymorphisms (SNPs) (rs11763759, rs4720470, rs2072183, rs2073547) of NPC1L1, and three SNPs (rs12916, rs2303151, rs4629571) of HMGCR were genotyped in 872 PTVD patients. According to the coronary angiography results, patients were divided into low-degree CAC group and high-degree CAC group.

RESULTS

A total of 872 PTVD patients (mean age, 47.71 ± 6.12; male, 72.8%) were finally included for analysis. Multivariate logistic regression analysis showed no significant association between the SNPs of NPC1L1 and HMGCR genes and high-degree CAC in the total population (P > 0.05). Subgroup analysis by gender revealed that the variant genotype (TT/CT) of rs4720470 on NPC1L1 gene was associated with increased risk for high-degree CAC in male patients only (OR = 1.505, 95% CI: 1.008-2.249, P = 0.046) in dominant model, but no significant association was found in female population, other SNPs of NPC1L1 and HMGCR genes (all P > 0.05).

CONCLUSIONS

We reported for the first time that the rs4720470 on NPC1L1 gene was associated with high-degree CAC in male patients with PTVD. In the future, whether therapies related to this target could reduce CAC and cardiovascular events deserves further investigation.

Bayesian LASSO for population stratification correction in rare haplotype association studies

Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.

HMGCR gene polymorphism is associated with residual cholesterol risk in premature triple-vessel disease patients treated with moderate-intensity statins

BACKGROUND

To investigate the association of HMGCR and NPC1L1 gene polymorphisms with residual cholesterol risk (RCR) in patients with premature triple-vessel disease (PTVD).

METHODS

Three SNPs within HMGCR including rs12916, rs2303151, and rs4629571, and four SNPs within NPC1L1 including rs11763759, rs4720470, rs2072183, and rs2073547 were genotyped. RCR was defined as achieved low-density lipoprotein cholesterol (LDL-C) concentrations after statins higher than 1.8 mmol/L (70 mg/dL).

RESULTS

Finally, a total of 609 PTVD patients treated with moderate-intensity statins were included who were divided into two groups: non-RCR group (n = 88) and RCR group (n = 521) according to LDL-C concentrations. Multivariate logistic regression showed the homozygotes for the minor allele of rs12916 within HMGCR gene (CC) were associated with a 2.08 times higher risk of RCR in recessive model [odds ratio (OR): 2.08, 95% confidence interval (CI): 1.16-3.75]. In codominant model, the individuals homozygous for the minor allele of rs12916 (CC) were associated with a 2.26 times higher risk of RCR (OR: 2.26, 95% CI: 1.16-4.43) while the heterozygous individuals (CT) were not, compared with the individuals homozygous for the major allele of rs12916 (TT). There was no significant association between the SNPs within NPC1L1 gene and RCR in various models.

CONCLUSIONS

We first reported that the variant homozygous CC of rs12916 within HMGCR gene may incur a significantly higher risk of RCR in PTVD patients treated with statins, providing new insights into early individualized guidance of precise lipid-lowering treatment.

Study of effect modifiers of genetically predicted CETP reduction

Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and body mass index (BMI) to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher high-density lipoprotein cholesterol and lower low-density lipoprotein cholesterol for the same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex differences in cardiovascular outcomes in our data. Our results provide insight into the clinical effects of CETP inhibitors in the presence of effect modification based on genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.

Menopause modulates the circulating metabolome: evidence from a prospective cohort study

AIMS

We studied the changes in the circulating metabolome and their relation to the menopausal hormonal shift in 17β-oestradiol and follicle-stimulating hormone levels among women transitioning from perimenopause to early postmenopause.

METHODS AND RESULTS

We analysed longitudinal data from 218 Finnish women, 35 of whom started menopausal hormone therapy during the study. The menopausal transition was monitored with menstrual diaries and serum hormone measurements. The median follow-up was 14 months (interquartile range: 8-20). Serum metabolites were quantified with targeted nuclear magnetic resonance metabolomics. The model results were adjusted for age, follow-up duration, education, lifestyle, and multiple comparisons. Menopause was associated with 85 metabolite measures. The concentration of apoB (0.17 standard deviation [SD], 99.5% confidence interval [CI] 0.03-0.31), very-low-density lipoprotein triglycerides (0.25 SD, CI 0.05-0.45) and particles (0.21 SD, CI 0.05-0.36), low-density lipoprotein (LDL) cholesterol (0.17 SD, CI 0.01-0.34) and particles (0.17 SD, CI 0.03-0.31), high-density lipoprotein (HDL) triglycerides (0.24 SD, CI 0.02-0.46), glycerol (0.32 SD, CI 0.07-0.58) and leucine increased (0.25 SD, CI 0.02-0.49). Citrate (-0.36 SD, CI -0.57 to -0.14) and 3-hydroxybutyrate concentrations decreased (-0.46 SD, CI -0.75 to -0.17). Most metabolite changes were associated with the menopausal hormonal shift. This explained 11% and 9% of the LDL cholesterol and particle concentration increase, respectively. Menopausal hormone therapy was associated with increased medium-to-large HDL particle count and decreased small-to-medium LDL particle and glycine concentration.

CONCLUSIONS

Menopause is associated with proatherogenic circulating metabolome alterations. Female sex hormones levels are connected to the alterations, highlighting their impact on women's cardiovascular health.

Apt interpretation of comprehensive lipoprotein data in large-scale epidemiology: disclosure of fundamental structural and metabolic relationships

BACKGROUND

Quantitative lipoprotein analytics using nuclear magnetic resonance (NMR) spectroscopy is currently commonplace in large-scale studies. One methodology has become widespread and is currently being utilized also in large biobanks. It allows the comprehensive characterization of 14 lipoprotein subclasses, clinical lipids, apolipoprotein A-I and B. The details of these data are conceptualized here in relation to lipoprotein metabolism with particular attention on the fundamental characteristics of subclass particle numbers, lipid concentrations and compositional measures.

METHODS AND RESULTS

The NMR methodology was applied to fasting serum samples from Northern Finland Birth Cohorts 1966 and 1986 with 5651 and 5605 participants, respectively. All results were highly consistent between the cohorts. Circulating lipid concentrations in a particular lipoprotein subclass arise predominantly as the result of the circulating number of those subclass particles. The spherical lipoprotein particle shape, with a radially oriented surface monolayer, imposes size-dependent biophysical constraints for the lipid composition of individual subclass particles and inherently restricts the accommodation of metabolic changes via compositional modifications. The new finding that the relationship between lipoprotein subclass particle concentrations and the particle size is log-linear reveals that circulating lipoprotein particles are also under rather strict metabolic constraints for both their absolute and relative concentrations.

CONCLUSIONS

The fundamental structural and metabolic relationships between lipoprotein subclasses elucidated in this study empower detailed interpretation of lipoprotein metabolism. Understanding the intricate details of these extensive data is important for the precise interpretation of novel therapeutic opportunities and for fully utilizing the potential of forthcoming analyses of genetic and metabolic data in large biobanks.

Correlation Between Plasma Proteomics and Adverse Outcomes Among Older Men With Chronic Coronary Syndrome

Background

Chronic coronary syndrome (CCS) is a newly proposed concept and is hallmarked by more long-term major adverse cardiovascular events (MACEs), calling for accurate prognostic biomarkers for initial risk stratification.

Methods

Data-independent acquisition liquid chromatography tandem mass spectrometry (DIA LC-MS/MS) quantitative proteomics was performed on 38 patients with CCS; 19 in the CCS events group and 19 in the non-events group as the controls. We also developed a machine-learning-based pipeline to identify proteins as potential biomarkers and validated the target proteins by enzyme-linked immunosorbent assay in an independent prospective cohort.

Results

Fifty-seven differentially expressed proteins were identified by quantitative proteomics and three final biomarkers were preliminarily selected from the machine-learning-based pipeline. Further validation with the prospective cohort showed that endothelial protein C receptor (EPCR) and cholesteryl ester transfer protein (CETP) levels at admission were significantly higher in the CCS events group than they were in the non-events group, whereas the carboxypeptidase B2 (CPB2) level was similar in the two groups. In the Cox survival analysis, EPCR and CETP were independent risk factors for MACEs. We constructed a new prognostic model by combining the Framingham coronary heart disease (CHD) risk model with EPCR and CETP levels. This new model significantly improved the C-statistics for MACE prediction compared with that of the Framingham CHD risk model alone.

Conclusion

Plasma proteomics was used to find biomarkers of predicting MACEs in patients with CCS. EPCR and CETP were identified as promising prognostic biomarkers for CCS.

Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation

Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r2 = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r2 < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response.

Heparin binding triggers human VLDL remodeling by circulating lipoprotein lipase: Relevance to VLDL functionality in health and disease

Hydrolysis of VLDL triacylglycerol (TG) by lipoprotein lipase (LpL) is a major step in energy metabolism and VLDL-to-LDL maturation. Most functional LpL is anchored to the vascular endothelium, yet a small amount circulates on TG-rich lipoproteins. As circulating LpL has low catalytic activity, its role in VLDL remodeling is unclear. We use pre-heparin plasma and heparin-sepharose affinity chromatography to isolate VLDL fractions from normolipidemic, hypertriglyceridemic, or type-2 diabetic subjects. LpL is detected only in the heparin-bound fraction. Transient binding to heparin activates this VLDL-associated LpL, which hydrolyses TG, leading to gradual VLDL remodeling into IDL/LDL and HDL-size particles. The products and the timeframe of this remodeling closely resemble VLDL-to-LDL maturation in vivo. Importantly, the VLDL fraction that does not bind heparin is not remodeled. This relatively inert LpL-free VLDL is rich in TG and apoC-III, poor in apoE and apoC-II, shows impaired functionality as a substrate for the exogenous LpL or CETP, and likely has prolonged residence time in blood, which is expected to promote atherogenesis. This non-bound VLDL fraction increases in hypertriglyceridemia and in type-2 diabetes but decreases upon diabetes treatment that restores the glycemic control. In stark contrast, heparin binding by LDL increases in type-2 diabetes triggering pro-atherogenic LDL modifications. Therefore, the effects of heparin binding are associated negatively with atherogenesis for VLDL but positively for LDL. Collectively, the results reveal that binding to glycosaminoglycans initiates VLDL remodeling by circulating LpL, and suggest heparin binding as a marker of VLDL functionality and a readout for treatment of metabolic disorders.

Effect of NPC1L1 and HMGCR Genetic Variants With Premature Triple-Vessel Coronary Disease

Background: Both Niemann-Pick C1-like 1 (NPC1L1) and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) play a key role on dyslipidaemia. We aim to evaluate whether NPC1L1 and HMGCR genetic variants are associated with susceptibility of premature triple-vessel disease (PTVD).

Methods: Four single-nucleotide polymorphisms (SNPs) (rs11763759, rs4720470, rs2072183, and rs2073547) of NPC1L1; and three SNPs (rs12916, rs2303151, and rs4629571) of HMGCR were genotyped in 872 PTVD patients (males ≤ 50 years old and females ≤ 60 years old), and 401 healthy controls.

Results: After adjusting for age and sex, rs12916 of HMGCR was associated with the risk of PTVD in dominance model [odds ratio (OR) = 1.68, 95% confidence intervals (CI): 1.29–2.18, P < 0.001], recessive model (OR = 1.43, 95% CI: 1.08–1.90, P = 0.013) and codominant model (OR = 1.38, 95% CI: 1.17–1.63, P < 0.001); meanwhile, rs4720470 of NPC1L1 was related to increased risk of PTVD in recessive model (OR = 1.74, 95% CI: 1.14–2.74, P = 0.013). Patients who carried both variant rs4720470 and rs12916 also had the risk of PTVD (P < 0.001); however, there were no correlation between these SNPs and the SNYTAX score (all P > 0.05).

Conclusions: This is the first report that rs4720470 is a novel polymorphism of the NPC1L1 gene associated with PTVD, and rs12916 of HMGCR gene appears to be a strong genetic marker of PTVD. Our study may improve the early warning, therapeutic strategies and drug development of PTVD.

Genome-wide Association Study of Lipid Traits in Youth With Type 2 Diabetes

CONTEXT

Dyslipidemia is highly prevalent in youth with type 2 diabetes (T2D), yet the pathogenic components of dyslipidemia in youth with T2D are poorly understood.

OBJECTIVE

To evaluate the genetic determinants of lipid traits in youth with T2D through a genome-wide association study.

DESIGN PARTICIPANTS AND MAIN OUTCOME MEASURES

We genotyped 206 928 variants and imputed 17 642 824 variants in 1076 youth (mean age 15.0 ± 2.48 years) with T2D from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) and SEARCH for Diabetes in Youth (SEARCH) studies as part of the Progress in Diabetes Genetics in Youth (ProDiGY) consortium. We performed association testing for triglyceride and low-density lipoprotein cholesterol and high-density lipoprotein cholesterol (HDL-c) concentrations adjusted for the genetic relationship matrix within each substudy followed by meta-analyses for each trait.

RESULTS

We identified a novel association between a deletion on chromosome 3 (3:67817380_AT/A_Deletion:RP11-81N13.1) and triglyceride levels at genome-wide level of significance (P = 2.3 × 10-8) with each risk allele increasing triglycerides by 20%. We also identified a genome-wide significant signal at rs247617 (P = 5.1 × 10-9) between HERFUD1 and CETP associated with HDL-c, with carriers of 1 copy of the risk allele having twice higher HDL-c.

CONCLUSIONS

Our genetic analyses of lipid traits in youth with T2D have identified 1 novel and 1 previously known locus. Additional studies are needed to further characterize the genetic architecture of dyslipidemia in youth with T2D.

Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease

Development of cholesteryl ester transfer protein (CETP) inhibitors for coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and drug target Mendelian randomization of CETP protein concentration, comparing this to Mendelian randomization of proprotein convertase subtilisin/kexin type 9 (PCSK9). We show that previous failures of CETP inhibitors are likely compound related, as illustrated by significant degrees of between-compound heterogeneity in effects on lipids, blood pressure, and clinical outcomes observed in trials. On-target CETP inhibition, assessed through Mendelian randomization, is expected to reduce the risk of CHD, heart failure, diabetes, and chronic kidney disease, while increasing the risk of age-related macular degeneration. In contrast, lower PCSK9 concentration is anticipated to decrease the risk of CHD, heart failure, atrial fibrillation, chronic kidney disease, multiple sclerosis, and stroke, while potentially increasing the risk of Alzheimer's disease and asthma. Due to distinct effects on lipoprotein metabolite profiles, joint inhibition of CETP and PCSK9 may provide added benefit. In conclusion, we provide genetic evidence that CETP is an effective target for CHD prevention but with a potential on-target adverse effect on age-related macular degeneration.

A phenome-wide association study of genetically mimicked statins

BACKGROUND

Beyond their success in cardiovascular disease prevention, statins are increasingly recognized to have sex-specific pleiotropic effects. To gain additional insight, we characterized associations of genetically mimicked statins across the phenotype sex-specifically. We also assessed whether any apparently non-lipid effects identified extended to genetically mimicking other widely used lipid modifiers (proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and ezetimibe) or were a consequence of low-density lipoprotein cholesterol (LDL-c).

METHODS

We performed a sex-specific phenome-wide association study assessing the association of genetic variants in HMGCR, mimicking statins, with 1701 phenotypes. We used Mendelian randomization (MR) to assess if any non-lipid effects found were evident for genetically mimicked PCSK9 inhibitors and ezetimibe or for LDL-c.

RESULTS

As expected, genetically mimicking statins was inversely associated with LDL-c, apolipoprotein B (ApoB), and total cholesterol (TC) and positively associated with glycated hemoglobin (HbA1c) and was related to body composition. Genetically mimicking statins was also inversely associated with serum calcium, sex hormone-binding globulin (SHBG), and platelet count and positively associated with basal metabolic rate (BMR) and mean platelet volume. Stronger associations with genetically mimicked statins were evident for women than men for lipid traits (LDL-c, ApoB, and TC), calcium, and SHBG, but not for platelet attributes, body composition, or BMR. Genetically mimicking PCSK9 inhibitors or ezetimibe was also associated with lower lipids, but was not related to calcium, SHBG, BMR, or body composition. Genetically higher LDL-c increased lipids and decreased BMR, but did not affect calcium, HbA1c, platelet attributes, or SHBG with minor effects on body composition.

CONCLUSIONS

Similar inverse associations were found for genetically mimicking statins on lipid traits in men and women as for other lipid modifiers. Besides the positive associations with HbA1c, BMI (which may explain the higher BMR), and aspects of body composition in men and women, genetically mimicking statins was additionally associated with platelet attributes in both sexes and was inversely associated with serum calcium and SHBG in women. This genetic evidence suggests potential pathways that contribute to the effects of statins particularly in women. Further investigation is needed to confirm these findings and their implications for clinical practice.

Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development

Drug development in cardiovascular disease is stagnating, with lack of efficacy and adverse effects being barriers to innovation. Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization, with genetic support for causation increasing the probability of a clinical trial succeeding. Mendelian randomization applied to quantitative traits can identify risk factors for disease that are both causal and amenable to therapeutic modification. However, important differences exist between genetic investigations of a biomarker (such as HDL cholesterol) and a drug target aimed at modifying the same biomarker of interest (such as cholesteryl ester transfer protein), with implications for the methodology, interpretation and application of Mendelian randomization to drug development. Differences include the comparative nature of the genetic architecture - that is, biomarkers are typically polygenic, whereas protein drug targets are influenced by either cis-acting or trans-acting genetic variants - and the potential for drug targets to show disease associations that might differ from those of the biomarker that they are intended to modify (target-mediated pleiotropy). In this Review, we compare and contrast the use of Mendelian randomization to evaluate potential drug targets versus quantitative traits. We explain how genetic epidemiological studies can be used to assess the aetiological roles of biomarkers in disease and to prioritize drug targets, including designing their evaluation in clinical trials.

Apolipoprotein B: the Rosetta Stone of lipidology

PURPOSE OF REVIEW

This review summarizes the evidence that apolipoprotein B (apoB) integrates the conventional lipid markers - total cholesterol, triglycerides, LDL-cholesterol, and non-HDL-cholesterol - into a single index that accurately and simply quantitates the atherogenic risk due to the apoB lipoprotein particles.

RECENT FINDINGS

Marked hypertriglyceridemia remains the essential signal for hyperchylomicronemia and potential pancreatitis. However, with the exception of Lp(a) and the abnormal cholesterol-enriched remnant particles that are the hallmark of type III hyperlipoproteinemia, recent evidence from discordance analyses and Mendelian randomization indicate that apoB integrates the risk due to the atherogenic lipoprotein particles because all LDL particles are, within the limits of our ability to measure any differences, equally atherogenic and all, except the largest VLDL particles are, within the limits of our ability to measure any differences, equally atherogenic.

SUMMARY

Measuring apoB as well as the conventional lipids is essential for accurate diagnosis. For almost all follow-up, however, apoB is all that need be measured. ApoB is the Rosetta Stone of lipidology because dyslipoproteinemia cannot be understood unless apoB is measured.

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.

Comparison of calculated remnant lipoprotein cholesterol levels with levels directly measured by nuclear magnetic resonance

BACKGROUND

Remnant cholesterol (RC) can partly explain the residual risk in atherosclerotic cardiovascular disease (ASCVD). A consensus method of measuring RC levels has not been established yet. In clinical practice, RC levels are usually calculated from the standard lipid profile, which are not true RC. Nuclear magnetic resonance (NMR) can measure RC levels directly. This study aimed to characterize RC at fasting and non-fasting states in more details and establish the performance of calculated RC and NMR-measured RC.

METHODS

Blood samples at fasting state and at 2 h and 4 h postprandial states were collected in 98 subjects. Lipid parameters including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), subfractions 3, 4, and 5 of very low-density lipoprotein cholesterol (VLDL3-C, VLDL4-C, and VLDL5-C, respectively), and intermediate-density lipoprotein cholesterol (IDL-C) were measured by enzymatic method and NMR. RC levels calculated from the standard lipid profile or measured by NMR were referred here as RCe or RCn.

RESULTS

The RCe and RCn levels were different, but both of them increased after a meal (P < 0.05), especially at 4 h postprandial state. Low correlations were found between RCe and RCn in the 1st, 2nd, and 3rd quartiles of TG, but RCn showed great correlation with RCe in the highest quartile regardless of the fasting or non-fasting state (R = 0.611, 0.536, and 0.535 for 0 h, 2 h, and 4 h, respectively). However, across the 2nd and 3rd quartiles, RCe levels were nearly close to RCn levels. RCe levels tended to overestimate RCn levels in the 1st quartile of TGe levels with median differences of 0.23(- 0.13, 0.63) and underestimate RCn levels with median differences of - 0.23(- 0.33, 0.07) in the highest quartile of TGe levels.

CONCLUSIONS

RC calculated from the standard lipid profile as TC minus LDL-C minus HDL-C is different from the NMR-measured RC. According to different TG levels, RC could overestimate or underestimate the actual RC level. Developing a consensus clinical method to measure RC levels is necessary, so that results from different studies and platforms can be more directly compared.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR1900020873. Registered in 21 January 2019 - Retrospectively registered.

Cholesteryl Ester Transfer Protein and Lipid Metabolism and Cardiovascular Diseases

In this chapter, we present the major advances in CETP research since the detection, isolation, and characterization of its activity in the plasma of humans and several species. Since CETP is a major modulator of HDL plasma levels, the clinical importance of CETP activity was recognized very early. We describe the participation of CETP in reverse cholesterol transport, conflicting results in animal and human genetic studies, possible new functions of CETP, and the results of the main clinical trials on CETP inhibition. Despite major setbacks in clinical trials, the hypothesis that CETP inhibitors are anti-atherogenic in humans is still being tested.