Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment

Systemic lupus erythematosus patients have unique changes in serum metabolic profiles across age associated with cardiometabolic risk

OBJECTIVES

Cardiovascular disease through accelerated atherosclerosis is a leading cause of mortality for patients with systemic lupus erythematosus (SLE), likely due to increased chronic inflammation and cardiometabolic defects over age. We investigated age-associated changes in metabolomic profiles of SLE patients and healthy controls (HCs).

METHODS

Serum NMR metabolomic profiles from female SLE patients (n = 164, age = 14-76) and HCs (n = 123, age = 13-72) were assessed across age by linear regression and by age group between patients/HCs (Group 1, age ≤ 25, n = 62/46; Group 2, age = 26-49, n = 50/46; Group 3, age ≥ 50, n = 52/31) using multiple t tests. The impact of inflammation, disease activity and treatments were assessed, and UK Biobank disease-wide association analysis of metabolites was performed.

RESULTS

Age-specific metabolomic profiles were identified in SLE patients vs HCs, including reduced amino acids (Group 1), increased very-low-density lipoproteins (Group 2), and increased low-density lipoproteins (Group 3). Twenty-five metabolites were significantly altered in all SLE age groups, dominated by decreased atheroprotective high-density lipoprotein (HDL) subsets, HDL-bound apolipoprotein (Apo)A1 and increased glycoprotein acetyls (GlycA). Furthermore, ApoA1 and GlycA were differentially associated with disease activity and serological measures, as well as atherosclerosis incidence and myocardial infarction mortality risk through disease-wide association. Separately, glycolysis pathway metabolites (acetone/citrate/creatinine/glycerol/lactate/pyruvate) uniquely increased with age in SLE, significantly influenced by prednisolone (increased pyruvate/lactate) and hydroxychloroquine (decreased citrate/creatinine) treatment and associated with type 1 and type 2 diabetes by disease-wide association.

CONCLUSIONS

Increasing HDL (ApoA1) levels through therapeutic/nutritional intervention, whilst maintaining low disease activity, in SLE patients from a young age could improve cardiometabolic disease outcomes. Biomarkers from the glycolytic pathway could indicate adverse metabolic effects of current therapies.

Exploring Molecular Mechanisms and Biomarkers in COPD: An Overview of Current Advancements and Perspectives

Chronic obstructive pulmonary disease (COPD) plays a significant role in global morbidity and mortality rates, typified by progressive airflow restriction and lingering respiratory symptoms. Recent explorations in molecular biology have illuminated the complex mechanisms underpinning COPD pathogenesis, providing critical insights into disease progression, exacerbations, and potential therapeutic interventions. This review delivers a thorough examination of the latest progress in molecular research related to COPD, involving fundamental molecular pathways, biomarkers, therapeutic targets, and cutting-edge technologies. Key areas of focus include the roles of inflammation, oxidative stress, and protease-antiprotease imbalances, alongside genetic and epigenetic factors contributing to COPD susceptibility and heterogeneity. Additionally, advancements in omics technologies-such as genomics, transcriptomics, proteomics, and metabolomics-offer new avenues for comprehensive molecular profiling, aiding in the discovery of novel biomarkers and therapeutic targets. Comprehending the molecular foundation of COPD carries substantial potential for the creation of tailored treatment strategies and the enhancement of patient outcomes. By integrating molecular insights into clinical practice, there is a promising pathway towards personalized medicine approaches that can improve the diagnosis, treatment, and overall management of COPD, ultimately reducing its global burden.

Emerging clinical role of proprotein convertase subtilisin/kexin type 9 inhibition-Part two: Current and emerging concepts in the clinical use of PCSK9 inhibition

BACKGROUND

Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have emerged as a novel class of drugs with cardioprotective effects through their lipid-lowering effects.

OBJECTIVE

This review aims to discuss existing and novel strategies of PCSK9 inhibition, providing an overview of established randomized controlled trials and ongoing outcome trials that assess the efficacy and long-term safety of PCSK9 inhibitors. It also explores the evolving role of PCSK9 beyond lipid metabolism and outlines the pleiotropic actions of PCSK9 inhibition in various disorders and future directions including novel strategies to target PCSK9.

CONCLUSION

PCSK9 inhibition shows promise not only in lipid metabolism but also in other disease processes, including atherosclerotic plaque remodeling, acute coronary syndrome, stroke, inflammation, and immune response.

PCSK9 genetic variants and risk of vascular and non-vascular diseases in Chinese and UK populations

AIMS

Lowering low-density lipoprotein cholesterol (LDL-C) through PCSK9 inhibition represents a new therapeutic approach to preventing and treating cardiovascular disease (CVD). Phenome-wide analyses of PCSK9 genetic variants in large biobanks can help to identify unexpected effects of PCSK9 inhibition.

METHODS AND RESULTS

In the prospective China Kadoorie Biobank, we constructed a genetic score using three variants at the PCSK9 locus associated with directly measured LDL-C [PCSK9 genetic score (PCSK9-GS)]. Logistic regression gave estimated odds ratios (ORs) for PCSK9-GS associations with CVD and non-CVD outcomes, scaled to 1 SD lower LDL-C. PCSK9-GS was associated with lower risks of carotid plaque [n = 8340 cases; OR = 0.61 (95% confidence interval: 0.45-0.83); P = 0.0015], major occlusive vascular events [n = 15 752; 0.80 (0.67-0.95); P = 0.011], and ischaemic stroke [n = 11 467; 0.80 (0.66-0.98); P = 0.029]. However, PCSK9-GS was also associated with higher risk of hospitalization with chronic obstructive pulmonary disease [COPD: n = 6836; 1.38 (1.08-1.76); P = 0.0089] and with even higher risk of fatal exacerbations amongst individuals with pre-existing COPD [n = 730; 3.61 (1.71-7.60); P = 7.3 × 10-4]. We also replicated associations for a PCSK9 variant, reported in UK Biobank, with increased risks of acute upper respiratory tract infection (URTI) [pooled OR after meta-analysis of 1.87 (1.38-2.54); P = 5.4 × 10-5] and self-reported asthma [pooled OR of 1.17 (1.04-1.30); P = 0.0071]. There was no association of a polygenic LDL-C score with COPD hospitalization, COPD exacerbation, or URTI.

CONCLUSION

The LDL-C-lowering PCSK9 genetic variants are associated with lower risk of subclinical and clinical atherosclerotic vascular disease but higher risks of respiratory diseases. Pharmacovigilance studies may be required to monitor patients treated with therapeutic PCSK9 inhibitors for exacerbations of respiratory diseases or respiratory tract infections.

LAY SUMMARY

Genetic analyses of over 100 000 participants of the China Kadoorie Biobank, mimicking the effect of new drugs intended to reduce cholesterol by targeting the PCSK9 protein, have identified potential severe effects of lower PCSK9 activity in patients with existing respiratory disease.PCSK9 genetic variants that are associated with lower cholesterol and reduced rates of cardiovascular disease are also associated with increased risk of a range of respiratory diseases, including asthma, upper respiratory tract infections, and hospitalization with chronic obstructive pulmonary disease (COPD).These genetic variants are not associated with whether or not individuals have COPD; instead, they are specifically associated with an increase in the chance of those who already have COPD being hospitalized and even dying, suggesting that careful monitoring of such patients should be considered during development of and treatment with anti-PCSK9 medication.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Can acetate via FFA receptors contribute to the diabetogenic effect of statins?

Despite the proven effects of statins in preventing cardiovascular disease, their diabetogenic effect has caused concern. The mechanism of this diabetogenic effect is unknown. We suggest a novel mechanism that may contribute to the diabetogenic effect of statins, through an effect of statins that has apparently escaped previous consideration. Briefly, by inhibiting HMG-CoA reductase, statins may cause accumulation of acetate, which through FFA2 and FFA3 stimulation may inhibit insulin secretion.

Pyrroloquinoline quinone inhibits PCSK9-NLRP3 mediated pyroptosis of Leydig cells in obese mice

Abnormal lipid metabolism and chronic low-grade inflammation are the main traits of obesity. Especially, the molecular mechanism of concomitant deficiency in steroidogenesis-associated enzymes related to testosterone (T) synthesis of obesity dominated a decline in male fertility is still poorly understood. Here, we found that in vivo, supplementation of pyrroloquinoline quinone (PQQ) efficaciously ameliorated the abnormal lipid metabolism and testicular spermatogenic function from high-fat-diet (HFD)-induced obese mice. Moreover, the transcriptome analysis of the liver and testicular showed that PQQ supplementation not only inhibited the high expression of proprotein convertase subtilisin/Kexin type 9 (PCSK9) but also weakened the NOD-like receptor family pyrin domain containing 3 (NLRP3)-mediated pyroptosis, which both played a negative role in T synthesis of Leydig Cells (LCs). Eventually, the function and the pyroptosis of LCs cultured with palmitic acid in vitro were simultaneously benefited by suppressing the expression of NLRP3 or PCSK9 respectively, as well the parallel effects of PQQ were affirmed. Collectively, our data revealed that PQQ supplementation is a feasible approach to protect T synthesis from PCSK9-NLRP3 crosstalk-induced LCs' pyroptosis in obese men.

Do Triglyceride-Rich Lipoproteins Equal Low-Density Lipoproteins in Risk of ASCVD?

PURPOSE OF REVIEW

Recent large clinical trials have failed to show that triglyceride-rich lipoprotein-lowering therapies decrease the risk of atherosclerotic cardiovascular disease (ASCVD). In this review, we reconcile these findings with evidence showing that elevated levels of triglyceride-rich lipoproteins and the cholesterol they contain, remnant cholesterol, cause ASCVD alongside low-density lipoprotein (LDL) cholesterol.

RECENT FINDINGS

Results from observational epidemiology, genetic epidemiology, and randomized controlled trials indicate that lowering of remnant cholesterol and LDL cholesterol decrease ASCVD risk by a similar magnitude per 1 mmol/L (39 mg/dL) lower non-high-density lipoprotein cholesterol (remnant cholesterol+LDL cholesterol). Indeed, recent guidelines for ASCVD prevention recommend the use of non-high-density lipoprotein cholesterol instead of LDL cholesterol. Current consensus is moving towards recognizing remnant cholesterol and LDL cholesterols as equals per 1 mmol/L (39 mg/dL) higher levels in the risk assessment of ASCVD; hence, triglyceride-rich lipoprotein-lowering therapies should also lower levels of non-HDL cholesterol to reduce ASCVD risk.

Assessing the safety of lipid-modifying medications among Chinese adolescents: a drug-target Mendelian randomization study

BACKGROUND

With increasing hypercholesterolemia prevalence in East Asian adolescents, pharmacologic interventions (e.g., HMGCR inhibitors (statins) and PCSK9 inhibitors) may have to be considered although their longer-term safety in the general adolescent population is unclear. This study aims to investigate the longer-term safety of HMGCR inhibitors and PCSK9 inhibitors among East Asian adolescents using genetics.

METHODS

A drug-target Mendelian randomization study leveraging the Global Lipid Genetics Consortium (East Asian, n = 146,492) and individual-level data from Chinese participants in the Biobank clinical follow-up of Hong Kong's "Children of 1997" birth cohort (n = 3443, aged ~ 17.6 years). Safety outcomes (n = 100) included anthropometric and hematological traits, renal, liver, lung function, and other nuclear magnetic resonance metabolomics. Positive control outcomes were cholesterol markers from the "Children of 1997" birth cohort and coronary artery disease from Biobank Japan.

RESULTS

Genetic inhibition of HMGCR and PCSK9 were associated with reduction in cholesterol-related NMR metabolomics, e.g., apolipoprotein B (HMGCR: beta [95% CI], - 1.06 [- 1.52 to - 0.60]; PCSK9: - 0.93 [- 1.56 to - 0.31]) and had the expected effect on the positive control outcomes. After correcting for multiple comparisons (p-value < 0.006), genetic inhibition of HMGCR was associated with lower linoleic acid - 0.79 [- 1.25 to - 0.35]. Genetic inhibition of PCSK9 was not associated with the safety outcomes assessed.

CONCLUSIONS

Statins and PCSK9 inhibitors in East Asian adolescents appeared to be safe based on the outcomes concerned. Larger studies were warranted to verify these findings. This study serves as a proof of principle study to inform the medication safety among adolescents via genetics.

Low levels of small HDL particles predict but do not influence risk of sepsis

BACKGROUND

Low levels of high-density lipoprotein (HDL) cholesterol have been associated with higher rates and severity of infection. Alterations in inflammatory mediators and infection are associated with alterations in HDL cholesterol. It is unknown whether the association between HDL and infection is present for all particle sizes, and whether the observed associations are confounded by IL-6 signalling.

METHODS

In the UK Biobank, ~ 270,000 individuals have data on HDL subclasses derived from nuclear magnetic resonance analysis. We estimated the association of particle count of total HDL and HDL subclasses (small, medium, large, and extra-large HDL) with sepsis, sepsis-related death, and critical care admission in a Cox regression model. We subsequently utilised genetic data from UK Biobank and FinnGen to perform Mendelian randomisation (MR) of each HDL subclass and sepsis to test for a causal relationship. Finally, we explored the role of IL-6 signalling as a potential causal driver of changes in HDL subclasses.

RESULTS

In observational analyses, higher particle count of small HDL was associated with protection from sepsis (Hazard ratio, HR 0.80; 95% CI 0.74-0.86, p = 4 × 10-9 comparing Quartile 4, highest quartile of HDL to Quartile 1, lowest quartile of HDL), sepsis-related death (HR 0.80; 95% CI 0.74-0.86, p = 2 × 10-4), and critical care admission with sepsis (HR 0.72 95% CI 0.60-0.85, p = 2 × 10-4). Parallel associations with other HDL subclasses were likely driven by changes in the small HDL compartment. MR analyses did not strongly support causality of small HDL particle count on sepsis incidence (Odds ratio, OR 0.98; 95% CI 0.89-1.07, p = 0.6) or death (OR 0.94, 95% CI 0.75-1.17, p = 0.56), although the estimate on critical care admission with sepsis supported protection (OR 0.73, 95% CI 0.57-0.95, p = 0.02). Bidirectional MR analyses suggested that increased IL-6 signalling was associated with reductions in both small (beta on small HDL particle count - 0.16, 95% CI - 0.10 to - 0.21 per natural log change in SD-scaled CRP, p = 9 × 10-8).and total HDL particle count (beta - 0.13, 95% CI - 0.09 to - 0.17, p = 7 × 10-10), but that the reverse effect of HDL on IL-6 signalling was largely null.

CONCLUSIONS

Low number of small HDL particles are associated with increased hazard of sepsis, sepsis-related death, and sepsis-related critical care admission. However, genetic analyses did not strongly support this as causal. Instead, we demonstrate that increased IL-6 signalling, which is known to alter infection risk, could confound associations with reduced HDL particle count, and suggest this may explain part of the observed association between (small) HDL particle count and sepsis.

Towards clinical application of GlycA and GlycB for early detection of inflammation associated with (pre)diabetes and cardiovascular disease: recent evidence and updates

Cardiometabolic diseases are associated with low-grade inflammation early in life and persists into old age. The long latency period presents opportunities for early detection, lifestyle modification and intervention. However, the performance of conventional biomarker assays to detect low-grade inflammation has been variable, particularly for early-stage cardiometabolic disorder including prediabetes and subclinical atherosclerotic vascular inflammation. During the last decade, the application of nuclear magnetic resonance (NMR) spectroscopy for metabolic profiling of biofluids in translational and epidemiological research has advanced to a stage approaching clinical application. Proton (1H)-NMR profiling induces no destructible physical changes to specimens, and generates quantitative signals from deconvoluted spectra that are highly repeatable and reproducible. Apart from quantitative analysis of amino acids, lipids/lipoproteins, metabolic intermediates and small proteins, 1H-NMR technology is unique in being able to detect composite signals of acute-phase and low-grade inflammation indicated by glycosylated acetyls (GlycA) and N-acetylneuraminic acid (sialic acid) moieties (GlycB). Different from conventional immunoassays that target epitopes and are susceptible to conformational variation in protein structure and binding, GlycA and GlycB signals are stable over time, and maybe complementary as well as superior to high-sensitivity C-reactive protein and other inflammatory cytokines. Here we review the physicochemical principles behind 1H-NMR profiling of GlycA and GlycB, and the available evidence supporting their potential clinical application for the prediction of incident (pre)diabetes, cardiovascular disease, and adverse outcomes.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Genotyping and population characteristics of the China Kadoorie Biobank

The China Kadoorie Biobank (CKB) is a population-based prospective cohort of >512,000 adults recruited from 2004 to 2008 from 10 geographically diverse regions across China. Detailed data from questionnaires and physical measurements were collected at baseline, with additional measurements at three resurveys involving ∼5% of surviving participants. Analyses of genome-wide genotyping, for >100,000 participants using custom-designed Axiom arrays, reveal extensive relatedness, recent consanguinity, and signatures reflecting large-scale population movements from recent Chinese history. Systematic genome-wide association studies of incident disease, captured through electronic linkage to death and disease registries and to the national health insurance system, replicate established disease loci and identify 14 novel disease associations. Together with studies of candidate drug targets and disease risk factors and contributions to international genetics consortia, these demonstrate the breadth, depth, and quality of the CKB data. Ongoing high-throughput omics assays of collected biosamples and planned whole-genome sequencing will further enhance the scientific value of this biobank.

Statin Usage Increases White Matter Hyperintensities: A Post Hoc Analysis of SPRINT-MIND

BACKGROUND

Progression of white matter hyperintensities (WMHs), a radiographic marker of cerebral small vessel disease, occurs with uncontrolled conventional cerebrovascular risk factors. Less certain, however, is the influence of dyslipidemia and the impact of 3-hydroxy-3-methylglutaryl-coenzyme-A reductase inhibitors (statins) on WMH progression. The goal of this study was to evaluate the influence of statins on the progression of WMH over a 4-year interval.

METHODS

We performed a post hoc analysis of the SPRINT-MIND database on those with serial volumetric WMH data. WMH progression was calculated as the difference in WMH volume between the 2 scans and then segmented into tertiles due to rightward skew. We defined statin usage as no therapy (0% of visits), partial therapy (1% to 99% of visits) or full therapy (100% of visits) as logged during study visits. Analysis of variance and χ 2 tests were used for continuous and categorical variables with adjustments made for variables known to influence WMH development.

RESULTS

A total of 425 individuals were included in this study: 53% without statins use, 27% partial use, and 20% full use. Demographic characteristics and baseline WMH volumes were similar among the cohort. Those with full statin use were significantly more likely to be in the top tertile of WMH progression (adjusted odds ratio: 2.30, 95% confidence interval: 1.11-4.77, P =0.025), despite improvement in dyslipidemia.

CONCLUSIONS

SPRINT-MIND participants prescribed a statin were nearly 2.5 times more likely to be within the top tertile of WMH progression over 4 years, despite adjustment for synergistic risk factors and improvement in low-density lipoprotein.

Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges

Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades to investigate the mechanisms of action of these drugs, their pleiotropic effects, and their side effects, aiming to identify novel targets for future personalized medicine with an improvement of the efficacy and safety associated with the treatment. Pharmacometabolomics is a branch of metabolomics that is focused on the study of drug effects on metabolic pathways that are implicated in the variation of response to the treatment considering also the influences from a specific disease, environment, and concomitant pharmacological therapies. In this review, we summarized the most significant metabolomic studies on the effects of lipid-lowering therapies, including the most commonly used statins and fibrates to novel drugs or nutraceutical approaches. The integration of pharmacometabolomics data with the information obtained from the other "omics" approaches could help in the comprehension of the biological mechanisms underlying the use of lipid-lowering drugs in view of defining a precision medicine to improve the efficacy and reduce the side effects associated with the treatment.

Maternal prenatal cholesterol levels predict offspring weight trajectories during childhood in the Norwegian Mother, Father and Child Cohort Study

BACKGROUND

Numerous intrauterine factors may affect the offspring's growth during childhood. We aimed to explore if maternal and paternal prenatal lipid, apolipoprotein (apo)B and apoA1 levels are associated with offspring weight, length, and body mass index from 6 weeks to eight years of age. This has previously been studied to a limited extent.

METHODS

This parental negative control study is based on the Norwegian Mother, Father and Child Cohort Study and uses data from the Medical Birth Registry of Norway. We included 713 mothers and fathers with or without self-reported hypercholesterolemia and their offspring. Seven parental metabolites were measured by nuclear magnetic resonance spectroscopy, and offspring weight and length were measured at 12 time points. Data were analyzed by linear spline mixed models, and the results are presented as the interaction between parental metabolite levels and offspring spline (age).

RESULTS

Higher maternal total cholesterol (TC) level was associated with a larger increase in offspring body weight up to 8 years of age (0.03 ≤ P_interaction ≤ 0.04). Paternal TC level was not associated with change in offspring body weight (0.17 ≤ P_interaction ≤ 0.25). Higher maternal high-density lipoprotein cholesterol (HDL-C) and apoA1 levels were associated with a lower increase in offspring body weight up to 8 years of age (0.001 ≤ P_interaction ≤ 0.005). Higher paternal HDL-C and apoA1 levels were associated with a lower increase in offspring body weight up to 5 years of age but a larger increase in offspring body weight from 5 to 8 years of age (0.01 ≤ P_interaction ≤ 0.03). Parental metabolites were not associated with change in offspring height or body mass index up to 8 years of age (0.07 ≤ P_interaction ≤ 0.99).

CONCLUSIONS

Maternal compared to paternal TC, HDL-C, and apoA1 levels were more strongly and consistently associated with offspring body weight during childhood, supporting a direct intrauterine effect.

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank

Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.

Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.

PCSK9 Inhibitors Have Apolipoprotein C-III-Related Anti-Inflammatory Activity, Assessed by 1H-NMR Glycoprotein Profile in Subjects at High or very High Cardiovascular Risk

Atherosclerosis is a chronic inflammatory disease caused by the accumulation of cholesterol in the intima. Proprotein convertase subtilisin/kexin type 9 inhibitors (iPCSK9) can reduce low-density lipoprotein (LDL) cholesterol levels by 60%, but there is still no evidence that they can lower markers of systemic inflammation such as high-sensitivity C-reactive protein (hsCRP). Acute-phase serum glycoproteins are upregulated in the liver during systemic inflammation, and their role as inflammatory biomarkers is under clinical evaluation. In this observational study, we evaluate the effects of iPCSK9 on glycoproteins (Glyc) A, B and F. Thirty-nine patients eligible for iPCSK9 therapy were enrolled. One sample before and after one to six months of iPCSK9 therapy with alirocumab was obtained from each patient. Lipids, apolipoproteins, hsCRP and PCSK9 levels were measured by biochemical analyses, and the lipoprotein and glycoprotein profiles were measured by 1H nuclear magnetic resonance (1H-NMR). The PCSK9 inhibitor reduced total (36.27%, p < 0.001), LDL (55.05%, p < 0.001) and non-high-density lipoprotein (HDL) (45.11%, p < 0.001) cholesterol, apolipoprotein (apo) C-III (10%, p < 0.001), triglycerides (9.92%, p < 0.001) and glycoprotein signals GlycA (11.97%, p < 0.001), GlycB (3.83%, p = 0.017) and GlycF (7.26%, p < 0.001). It also increased apoA-I (2.05%, p = 0.043) and HDL cholesterol levels (11.58%, p < 0.001). Circulating PCSK9 levels increased six-fold (626.28%, p < 0.001). The decrease in Glyc signals positively correlated with the decrease in triglycerides and apoC-III. In conclusion, in addition to LDL cholesterol, iPCSK9 therapy also induces a reduction in systemic inflammation measured by 1H-NMR glycoprotein signals, which correlates with a decrease in triglycerides and apoC-III.

Genetic Mimicry Analysis Reveals the Specific Lipases Targeted by the ANGPTL3-ANGPTL8 Complex and ANGPTL4

Angiopoietin-like proteins, ANGPTL3, ANGPTL4, and ANGPTL8, are involved in regulating plasma lipids. In vitro and animal-based studies point to LPL and endothelial lipase (EL, LIPG) as key targets of ANGPTLs. To examine the ANGPTL mechanisms for plasma lipid modulation in humans, we pursued a genetic mimicry analysis of enhancing or suppressing variants in the LPL, LIPG, lipase C hepatic type (LIPC), ANGPTL3, ANGPTL4, and ANGPTL8 genes using data on 248 metabolic parameters derived from over 110,000 nonfasted individuals in the UK Biobank and validated in over 13,000 overnight fasted individuals from 11 other European populations. ANGPTL4 suppression was highly concordant with LPL enhancement but not HL or EL, suggesting ANGPTL4 impacts plasma metabolic parameters exclusively via LPL. The LPL-independent effects of ANGPTL3 suppression on plasma metabolic parameters showed a striking inverse resemblance with EL suppression, suggesting ANGPTL3 not only targets LPL but also targets EL. Investigation of the impact of the ANGPTL3-ANGPTL8 complex on plasma metabolite traits via the ANGPTL8 R59W substitution as an instrumental variable showed a much higher concordance between R59W and EL activity than between R59W and LPL activity, suggesting the R59W substitution more strongly affects EL inhibition than LPL inhibition. Meanwhile, when using a rare and deleterious protein-truncating ANGPTL8 variant as an instrumental variable, the ANGPTL3-ANGPTL8 complex was very LPL specific. In conclusion, our analysis provides strong human genetic evidence that the ANGPTL3-ANGPTL8 complex regulates plasma metabolic parameters, which is achieved by impacting LPL and EL. By contrast, ANGPTL4 influences plasma metabolic parameters exclusively via LPL.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

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.

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.

Lipoprotein profiles of fat distribution and its association with insulin sensitivity

BACKGROUND

Fat deposition is associated with adverse outcomes. Waist-to-hip (WHR) ratio is a simple feasible index to assess fat distribution. Lipoprotein particle composition in relation to WHR and to what extent their association is mediated by insulin sensitivity are less investigated.

METHODS

In 504 randomly recruited Flemish (mean age: 48.9 years; women: 51.6%), we analyzed the lipoprotein particle constitutions using nuclear magnetic resonance spectroscopy. WHR obesity described a WHR of ≥ 0.85 for women or 0.9 for men. Insulin sensitivity was evaluated by the homeostasis model assessment-estimated insulin resistance (HOMA-IR). SCORE-2 risk algorithm was applied to estimate 10-year cardiovascular risk. Statistical methods included multivariable-adjusted linear regression analysis, logistic regression analysis, and mediation analysis.

RESULTS

The prevalence of WHR obesity was 54.6%, approximately 3 times of BMI-determined obesity (19.1%). Individuals with WHR obesity had significantly higher metabolic complications, such as hypertension (57.1%), dyslipidemia (61.8%), and insulin resistance (14.2%). WHR and WHR obesity were positively associated with total very-low-density lipoprotein (VLDL) particle concentration, remnant cholesterol, and triglycerides, but were negatively associated with VLDL particle size (P ≤ 0.027), independent of body mass index and other covariates. WHR was inversely associated with total high-density lipoprotein (HDL) particle concentration, whereas WHR obesity was inversely associated with HDL cholesterol (P ≤ 0.039). Neither WHR nor WHR obesity was associated with the concentration of total low-density lipoprotein (LDL) particles, LDL particle size, and LDL cholesterol (P ≥ 0.089). In the mediation analysis, insulin sensitivity significantly mediated the effect of WHR on total VLDL particle concentration (mediation percentage: 37.0%), remnant cholesterol (47.7%), and HDL cholesterol (41.1%). Individuals with WHR obesity were at increased cardiovascular risk, regardless of LDL cholesterol (P ≤0.028). In WHR obesity, higher total VLDL particle concent36ration and remnant cholesterol, and lower HDL cholesterol were associated with an increased cardiovascular risk (P≤ 0.002).

CONCLUSIONS

Upper-body fat deposition was independently associated with an unfavorable lipoprotein profile, and insulin sensitivity significantly mediated this association. LDL cholesterol might underestimate lipid abnormality for people with upper-body obesity and lowering VLDL particles and remnant cholesterol might potentially reduce the residual cardiovascular risk.

Innovative Approaches to Assess Intermediate Cardiovascular Risk Subjects: A Review From Clinical to Metabolomics Strategies

Current risk stratification strategies for coronary artery disease (CAD) have low predictive value in asymptomatic subjects classified as intermediate cardiovascular risk. This is relevant because not all coronary events occur in individuals with traditional multiple risk factors. Most importantly, the first manifestation of the disease may be either sudden cardiac death or acute coronary syndrome, after rupture and thrombosis of an unstable non-obstructive atherosclerotic plaque, which was previously silent. The inaccurate stratification using the current models may ultimately subject the individual to excessive or insufficient preventive therapies. A breakthrough in the comprehension of the molecular mechanisms governing the atherosclerosis pathology has driven many researches toward the necessity for a better risk stratification. In this Review, we discuss how metabolomics screening integrated with traditional risk assessments becomes a powerful approach to improve non-invasive CAD subclinical diagnostics. In addition, this Review highlights the findings of metabolomics studies performed by two relevant analytical platforms in current use-mass spectrometry (MS) hyphenated to separation techniques and nuclear magnetic resonance spectroscopy (NMR) -and evaluates critically the challenges for further clinical implementation of metabolomics data. We also discuss the modern understanding of the pathophysiology of atherosclerosis and the limitations of traditional analytical methods. Our aim is to show how discriminant metabolites originated from metabolomics approaches may become promising candidate molecules to aid intermediate risk patient stratification for cardiovascular events and how these tools could successfully meet the demands to translate cardiovascular metabolic biomarkers into clinical settings.

Longitudinal Metabolic Profiling of Maternal Obesity, Gestational Diabetes, and Hypertensive Pregnancy Disorders

CONTEXT

Comprehensive assessment of metabolism in maternal obesity and pregnancy disorders can provide information about the shared maternal-fetal milieu and give insight into both maternal long-term health and intergenerational transmission of disease burden.

OBJECTIVE

To assess levels, profiles, and change in the levels of metabolic measures during pregnancies complicated by obesity, gestational diabetes (GDM), or hypertensive disorders.

DESIGN, SETTING AND PARTICIPANTS

A secondary analysis of 2 study cohorts, PREDO and RADIEL, including 741 pregnant women.

MAIN OUTCOME MEASURES

We assessed 225 metabolic measures by nuclear magnetic resonance in blood samples collected at median 13 [interquartile range (IQR) 12.4-13.7], 20 (IQR 19.3-23.0), and 28 (27.0-35.0) weeks of gestation.

RESULTS

Across all 3 time points women with obesity [body mass index (BMI) ≥ 30kg/m2] in comparison to normal weight (BMI 18.5-24.99 kg/m2) had significantly higher levels of most very-low-density lipoprotein-related measures, many fatty and most amino acids, and more adverse metabolic profiles. The change in the levels of most metabolic measures during pregnancy was smaller in obese than in normal weight women. GDM, preeclampsia, and chronic hypertension were associated with metabolic alterations similar to obesity. The associations of obesity held after adjustment for GDM and hypertensive disorders, but many of the associations with GDM and hypertensive disorders were rendered nonsignificant after adjustment for BMI and the other pregnancy disorders.

CONCLUSIONS

This study shows that the pregnancy-related metabolic change is smaller in women with obesity, who display metabolic perturbations already in early pregnancy. Metabolic alterations of obesity and pregnancy disorders resembled each other suggesting a shared metabolic origin.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Effects of dexmedetomidine, propofol, sevoflurane and S-ketamine on the human metabolome: A randomised trial using nuclear magnetic resonance spectroscopy

BACKGROUND

Pharmacometabolomics uses large-scale data capturing methods to uncover drug-induced shifts in the metabolic profile. The specific effects of anaesthetics on the human metabolome are largely unknown.

OBJECTIVE

We aimed to discover whether exposure to routinely used anaesthetics have an acute effect on the human metabolic profile.

DESIGN

Randomised, open-label, controlled, parallel group, phase IV clinical drug trial.

SETTING

The study was conducted at Turku PET Centre, University of Turku, Finland, 2016 to 2017.

PARTICIPANTS

One hundred and sixty healthy male volunteers were recruited. The metabolomic data of 159 were evaluable.

INTERVENTIONS

Volunteers were randomised to receive a 1-h exposure to equipotent doses (EC50 for verbal command) of dexmedetomidine (1.5 ng ml-1; n = 40), propofol (1.7 μg ml-1; n = 40), sevoflurane (0.9% end-tidal; n = 39), S-ketamine (0.75 μg ml-1; n = 20) or placebo (n = 20).

MAIN OUTCOME MEASURES

Metabolite subgroups of apolipoproteins and lipoproteins, cholesterol, glycerides and phospholipids, fatty acids, glycolysis, amino acids, ketone bodies, creatinine and albumin and the inflammatory marker GlycA, were analysed with nuclear magnetic resonance spectroscopy from arterial blood samples collected at baseline, after anaesthetic administration and 70 min postanaesthesia.

RESULTS

All metabolite subgroups were affected. Statistically significant changes vs. placebo were observed in 11.0, 41.3, 0.65 and 3.9% of the 155 analytes in the dexmedetomidine, propofol, sevoflurane and S-ketamine groups, respectively. Dexmedetomidine increased glucose, decreased ketone bodies and affected lipoproteins and apolipoproteins. Propofol altered lipoproteins, fatty acids, glycerides and phospholipids and slightly increased inflammatory marker glycoprotein acetylation. Sevoflurane was relatively inert. S-ketamine increased glucose and lactate, whereas branched chain amino acids and tyrosine decreased.

CONCLUSION

A 1-h exposure to moderate doses of routinely used anaesthetics led to significant and characteristic alterations in the metabolic profile. Dexmedetomidine-induced alterations mirror α2-adrenoceptor agonism. Propofol emulsion altered the lipid profile. The inertness of sevoflurane might prove useful in vulnerable patients. S-ketamine induced amino acid alterations might be linked to its suggested antidepressive properties.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02624401. URL: https://clinicaltrials.gov/ct2/show/NCT02624401.

Glycosylation and Cardiovascular Diseases

Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting for approximately 18 million deaths in 2017. Coronary artery disease is the predominant cause of death from CVD, followed by stroke. Owing to recent technological advancements, glycans and glycosylation patterns of proteins have been investigated in association with CVD risk factors and clinical events. These studies have found significant associations of glycans as biomarkers of systemic inflammation and major CVD risk factors and events. While more limited, studies have also shown that glycans may be useful for monitoring response to anti-inflammatory therapies and may be responsive to changes in lifestyle, particularly in patients with chronic inflammatory diseases. Glycans capture summative risk information related to inflammatory, immune, and signaling pathways and are promising biomarkers for CVD risk prediction and therapeutic monitoring.

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.

Childhood overeating is associated with adverse cardiometabolic and inflammatory profiles in adolescence

Childhood eating behaviour contributes to the rise of obesity and related noncommunicable disease worldwide. However, we lack a deep understanding of biochemical alterations that can arise from aberrant eating behaviour. In this study, we prospectively associate longitudinal trajectories of childhood overeating, undereating, and fussy eating with metabolic markers at age 16 years to explore adolescent metabolic alterations related to specific eating patterns in the first 10 years of life. Data are from the Avon Longitudinal Study of Parents and Children (n = 3104). We measure 158 metabolic markers with a high-throughput (1H) NMR metabolomics platform. Increasing childhood overeating is prospectively associated with an adverse cardiometabolic profile (i.e., hyperlipidemia, hypercholesterolemia, hyperlipoproteinemia) in adolescence; whereas undereating and fussy eating are associated with lower concentrations of the amino acids glutamine and valine, suggesting a potential lack of micronutrients. Here, we show associations between early behavioural indicators of eating and metabolic markers.

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.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

Novel associations between parental and newborn cord blood metabolic profiles in the Norwegian Mother, Father and Child Cohort Study

BACKGROUND

More than one third of Norwegian women and men between 20 and 40 years of age have elevated cholesterol concentration. Parental metabolic health around conception or during pregnancy may affect the offspring's cardiovascular disease risk. Lipids are important for fetal development, but the determinants of cord blood lipids have scarcely been studied. We therefore aimed to describe the associations between maternal and paternal peri-pregnancy lipid and metabolic profile and newborn cord blood lipid and metabolic profile.

METHODS

This study is based on 710 mother-father-newborn trios from the Norwegian Mother, Father and Child Cohort Study (MoBa) and uses data from the Medical Birth Registry of Norway (MBRN). The sample included in this study consisted of parents with and without self-reported hypercholesterolemia the last 6 months before pregnancy and their partners and newborns. Sixty-four cord blood metabolites detected by nuclear magnetic resonance spectroscopy were analyzed by linear mixed model analyses. The false discovery rate procedure was used to correct for multiple testing.

RESULTS

Among mothers with hypercholesterolemia, maternal and newborn plasma high-density lipoprotein cholesterol, apolipoprotein A1, linoleic acid, docosahexaenoic acid, alanine, glutamine, isoleucine, leucine, valine, creatinine, and particle concentration of medium high-density lipoprotein were significantly positively associated (0.001 ≤ q ≤ 0.09). Among mothers without hypercholesterolemia, maternal and newborn linoleic acid, valine, tyrosine, citrate, creatinine, high-density lipoprotein size, and particle concentration of small high-density lipoprotein were significantly positively associated (0.02 ≤ q ≤ 0.08). Among fathers with hypercholesterolemia, paternal and newborn ratio of apolipoprotein B to apolipoprotein A1 were significantly positively associated (q = 0.04). Among fathers without hypercholesterolemia, no significant associations were found between paternal and newborn metabolites. Sex differences were found for many cord blood lipids.

CONCLUSIONS

Maternal and paternal metabolites and newborn sex were associated with several cord blood metabolites. This may potentially affect the offspring's long-term cardiovascular disease risk.

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in "Personalized Medicine" and "Innovative Biomedical Technologies") could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

Serum Metabolomic Signatures Can Predict Subclinical Atherosclerosis in Patients With Systemic Lupus Erythematosus

[Figure: see text].

Metabolomics and cardiovascular imaging: a combined approach for cardiovascular ageing

The purpose of this review is to explore how metabolomics can help uncover new biomarkers and mechanisms for cardiovascular ageing. Cardiovascular ageing refers to cardiovascular structural and functional alterations that occur with chronological ageing and that can lead to the development of cardiovascular disease. These alterations, which were previously only detectable on tissue histology or corroborated on blood samples, are now detectable with modern imaging techniques. Despite the emergence of powerful new imaging tools, clinical investigation into cardiovascular ageing is challenging because ageing is a life course phenomenon involving known and unknown risk factors that play out in a dynamic fashion. Metabolomic profiling measures large numbers of metabolites with diverse chemical properties. Metabolomics has the potential to capture changes in biochemistry brought about by pathophysiologic processes as well as by normal ageing. When combined with non-invasive cardiovascular imaging tools, metabolomics can be used to understand pathological consequences of cardiovascular ageing. This review will summarize previous metabolomics and imaging studies in cardiovascular ageing. These methods may be a clinically relevant and novel approach to identify mechanisms of cardiovascular ageing and formulate or personalize treatment strategies.

Investigating the relationships between unfavourable habitual sleep and metabolomic traits: evidence from multi-cohort multivariable regression and Mendelian randomization analyses

BACKGROUND

Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease.

METHODS

We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions.

RESULTS

We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures.

CONCLUSIONS

Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.

PCSK9 Levels and Metabolic Profiles in Elderly Subjects with Different Glucose Tolerance under Statin Therapy

Proprotein convertase subtilisin/kexin type 9 (PCSK9) degrades low-density lipoprotein cholesterol (LDL-C) receptors, and thus regulates the LDL-C levels in the circulation. Type 2 diabetics often have elevated LDL-C levels. However, the functions of PCSK9 in patients with alterations of glu-cose metabolism and statin therapy are still unclear. Method: we investigated a large cohort of 608 subjects, born in 1945 in Oulu, Finland (Oulu Cohort 1945). We studied the effects of PSCK9 lev-els with different glucose tolerances (normal glucose tolerance (NGT), prediabetes (PreDM) or type 2 diabetes (T2D)) with and without statin medication, and analyzed clinical data, NMR metabolomics and PCSK9 plasma levels. Results: PCSK9 plasma levels did not significantly differ between the three groups. Statin therapy significantly increased the PCSK9 levels in NGT, PreDM and T2D groups compared with subjects with no statins. In the NGT group, negative associations between PCSK9 and LDL-C, intermediate-density lipoprotein cholesterol (IDL-C), very low-density lipoprotein cholesterol (VLDL-C), total cholesterol and LDL and IDL triglycerides were observed under statin medication. In contrast, in the PreDM and T2D groups, these associa-tions were lost. Conclusions: our data suggest that in subjects with abnormal glucose metabolism and statin therapy, the significant PCSK9-mediated effects on the lipid metabolites are lost com-pared to NGT subjects, but statins reduced the LDL-C and VLDL-C levels.

Increased apolipoprotein-B:A1 ratio predicts cardiometabolic risk in patients with juvenile onset SLE

BACKGROUND

Cardiovascular disease is a leading cause of mortality in patients with juvenile-onset systemic lupus erythematosus (JSLE). Traditional factors for cardiovascular risk (CVR) prediction are less robust in younger patients. More reliable CVR biomarkers are needed for JSLE patient stratification and to identify therapeutic approaches to reduce cardiovascular morbidity and mortality in JSLE.

METHODS

Serum metabolomic analysis (including >200 lipoprotein measures) was performed on a discovery (n=31, median age 19) and validation (n=31, median age 19) cohort of JSLE patients. Data was analysed using cluster, receiver operating characteristic analysis and logistic regression. RNA-sequencing assessed gene expression in matched patient samples.

FINDINGS

Hierarchical clustering of lipoprotein measures identified and validated two unique JSLE groups. Group-1 had an atherogenic and Group-2 had an atheroprotective lipoprotien profile. Apolipoprotein(Apo)B:ApoA1 distinguished the two groups with high specificity (96.2%) and sensitivity (96.7%). JSLE patients with high ApoB:ApoA1 ratio had increased CD8+ T-cell frequencies and a CD8+ T-cell transcriptomic profile enriched in genes associated with atherogenic processes including interferon signaling. These metabolic and immune signatures overlapped statistically significantly with lipid biomarkers associated with sub-clinical atherosclerosis in adult SLE patients and with genes overexpressed in T-cells from human atherosclerotic plaque respectively. Finally, baseline ApoB:ApoA1 ratio correlated positively with SLE disease activity index (r=0.43, p=0.0009) and negatively with Lupus Low Disease Activity State (r=-0.43, p=0.0009) over 5-year follow-up.

INTERPRETATION

Multi-omic analysis identified high ApoB:ApoA1 as a potential biomarker of increased cardiometabolic risk and worse clinical outcomes in JSLE. ApoB:ApoA1 could help identify patients that require increased disease monitoring, lipid modification or lifestyle changes.

FUNDING

Lupus UK, The Rosetrees Trust, British Heart Foundation, UCL & Birkbeck MRC Doctoral Training Programme and Versus Arthritis.

Association of Circulating Metabolites in Plasma or Serum and Risk of Stroke: Meta-analysis From 7 Prospective Cohorts

OBJECTIVE

To conduct a comprehensive analysis of circulating metabolites and incident stroke in large prospective population-based settings.

METHODS

We investigated the association of metabolites with risk of stroke in 7 prospective cohort studies including 1,791 incident stroke events among 38,797 participants in whom circulating metabolites were measured by nuclear magnetic resonance technology. The relationship between metabolites and stroke was assessed with Cox proportional hazards regression models. The analyses were performed considering all incident stroke events and ischemic and hemorrhagic events separately.

RESULTS

The analyses revealed 10 significant metabolite associations. Amino acid histidine (hazard ratio [HR] per SD 0.90, 95% confidence interval [CI] 0.85, 0.94; p = 4.45 × 10-5), glycolysis-related metabolite pyruvate (HR per SD 1.09, 95% CI 1.04, 1.14; p = 7.45 × 10-4), acute-phase reaction marker glycoprotein acetyls (HR per SD 1.09, 95% CI 1.03, 1.15; p = 1.27 × 10-3), cholesterol in high-density lipoprotein (HDL) 2, and several other lipoprotein particles were associated with risk of stroke. When focused on incident ischemic stroke, a significant association was observed with phenylalanine (HR per SD 1.12, 95% CI 1.05, 1.19; p = 4.13 × 10-4) and total and free cholesterol in large HDL particles.

CONCLUSIONS

We found association of amino acids, glycolysis-related metabolites, acute-phase reaction markers, and several lipoprotein subfractions with the risk of stroke. These findings support the potential of metabolomics to provide new insights into the metabolic changes preceding stroke.

Effect of Atorvastatin on blood ketone levels and glycemic control in patients with type 2 diabetes mellitus: A single arm pilot study

BACKGROUND AND AIMS

Cholesterol and ketone bodies are synthesized in liver from a common precursor acetyl coenzyme A (acetyl-CoA). Statins by inhibiting cholesterol synthesis may lead to accumulation of acetyl-CoA in hepatocytes and its diversion towards ketogenesis. Ketone bodies may act as alternative energy source thus sparing blood glucose and contributing to hyperglycemia. The present study aims to assess the effect of Atorvastatin therapy on blood ketone levels and glycemic control in patients with T2DM.

METHODS

Study included 24 statin naïve subjects with T2DM. They were prescribed tablet Atorvastatin at dose of 10 mg once daily at bedtime. Ongoing anti-diabetic medications were not changed. Estimation of blood ketones, urine ketones, fasting plasma glucose (FPG), post-prandial plasma glucose (PPG), glycated hemoglobin (HbA1c) and lipid parameters was carried out at baseline and at 3 months after starting Atorvastatin.

RESULTS

There was moderate but significant increase in blood ketones (0.16 ± 0.08 mmol/L vs. 0.26 ± 0.07 mmol/L; p-value = 0.0000), FPG (133.8 ± 17.91 mg/dL vs. 143.3 ± 22.99 mg/dL; p-value = 0.0016) and PPG (193.0 ± 36.54 mg/dL vs. 211.0 ± 49.51 mg/dL; p-value = 0.0344) after 3 months of Atorvastatin therapy. This was associated with significant reduction in serum total cholesterol and low density lipoprotein cholesterol.

CONCLUSION

Three months therapy with Atorvastatin at the dose of 10 mg once daily at bedtime in patients with T2DM resulted in moderate rise in blood ketone levels, FPG and PPG in addition to improvement in lipid parameters.

Investigating pleiotropic effects of statins on ischemic heart disease in the UK Biobank using Mendelian randomisation

We examined whether specifically statins, of the major lipid modifiers (statins, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and ezetimibe) have pleiotropic effects on ischemic heart disease (IHD) via testosterone in men or women. As a validation, we similarly assessed whether a drug that unexpectedly likely increases IHD also operates via testosterone. Using previously published genetic instruments we conducted a sex-specific univariable and multivariable Mendelian randomization study in the UK Biobank, including 179918 men with 25410 IHD cases and 212080 women with 12511 IHD cases. Of these three lipid modifiers, only genetically mimicking the effects of statins in men affected testosterone, which partly mediated effects on IHD. Correspondingly, genetically mimicking effects of anakinra on testosterone and IHD presented a reverse pattern to that for statins. These insights may facilitate the development of new interventions for cardiovascular diseases as well as highlighting the importance of sex-specific explanations, investigations, prevention and treatment.

EpiMetal: an open-source graphical web browser tool for easy statistical analyses in epidemiology and metabolomics

MOTIVATION

An intuitive graphical interface that allows statistical analyses and visualizations of extensive data without any knowledge of dedicated statistical software or programming.

IMPLEMENTATION

EpiMetal is a single-page web application written in JavaScript, to be used via a modern desktop web browser.

GENERAL FEATURES

Standard epidemiological analyses and self-organizing maps for data-driven metabolic profiling are included. Multiple extensive datasets with an arbitrary number of continuous and category variables can be integrated with the software. Any snapshot of the analyses can be saved and shared with others via a www-link. We demonstrate the usage of EpiMetal using pilot data with over 500 quantitative molecular measures for each sample as well as in two large-scale epidemiological cohorts (N >10 000).

AVAILABILITY

The software usage exemplar and the pilot data are open access online at [http://EpiMetal.computationalmedicine.fi]. MIT licensed source code is available at the Github repository at [https://github.com/amergin/epimetal].

Genetic and In Vitro Inhibition of PCSK9 and Calcific Aortic Valve Stenosis

The authors investigated whether PCSK9 inhibition could represent a therapeutic strategy in calcific aortic valve stenosis (CAVS). A meta-analysis of 10 studies was performed to determine the impact of the PCSK9 R46L variant on CAVS, and the authors found that CAVS was less prevalent in carriers of this variant (odds ratio: 0.80 [95% confidence interval: 0.70 to 0.91]; p = 0.0011) compared with noncarriers. PCSK9 expression was higher in the aortic valves of patients CAVS compared with control patients. In human valve interstitials cells submitted to a pro-osteogenic medium, PCSK9 levels increased and a PCSK9 neutralizing antibody significantly reduced calcium accumulation.

Proprotein convertase subtilisin/kexin type 9: an update on the cardiovascular outcome studies

Inhibitors of the 3-hydroxy-3-methylglutaryl coenzyme A reductase enzyme, statins, are powerful cholesterol-lowering medications and have provided outstanding contributions to the primary and secondary prevention of coronary heart disease. Low-density lipoprotein cholesterol (LDL-C) is one of the major modifiable cardiovascular risk factors, indeed, every 1.0 mmol/L (38.7 mg/dL) reduction in LDL cholesterolaemia corresponds to a 21% lowering in the risk of major vascular events. In this context, the pharmacological approach with PCSK9 monoclonal antibodies is considered a promising non-statin therapeutic option for the management of lipid disorders in patients with persistent cardiovascular risk, including patients with diabetes mellitus. Data from two large clinical trials have indisputably demonstrated the efficacy of alirocumab and evolocumab in preventive major adverse cardiovascular events in high risk, secondary-prevention patients with clinical manifestation of atherosclerotic cardiovascular diseases. Finally, PCSK9 monoclonal antibodies did not increase the risk of serious adverse events, neurocognitive events, new-onset of diabetes, muscle-related events, or myalgia.

Metabolic effects of PCSK9 inhibition with Evolocumab in subjects with elevated Lp(a)

Background

Epidemiological studies substantiated that subjects with elevated lipoprotein(a) [Lp(a)] have a markedly increased cardiovascular risk. Inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9) lowers both LDL cholesterol (LDL-C) as well as Lp(a), albeit modestly. Effects of PCSK9 inhibition on circulating metabolites such as lipoprotein subclasses, amino acids and fatty acids remain to be characterized.

Methods

We performed nuclear magnetic resonance (NMR) metabolomics on plasma samples derived from 30 individuals with elevated Lp(a) (> 150 mg/dL). The 30 participants were randomly assigned into two groups, placebo (N = 14) and evolocumab (N = 16). We assessed the effect of 16 weeks of evolocumab 420 mg Q4W treatment on circulating metabolites by running lognormal regression analyses, and compared this to placebo. Subsequently, we assessed the interrelationship between Lp(a) and 14 lipoprotein subclasses in response to treatment with evolocumab, by running multilevel multivariate regression analyses.

Results

On average, evolocumab treatment for 16 weeks resulted in a 17% (95% credible interval: 8 to 26%, P < 0.001) reduction of circulating Lp(a), coupled with substantial reduction of VLDL, IDL and LDL particles as well as their lipid contents. Interestingly, increasing concentrations of baseline Lp(a) were associated with larger reduction in triglyceride-rich VLDL particles after evolocumab treatment.

Conclusions

Inhibition of PCSK9 with evolocumab markedly reduced VLDL particle concentrations in addition to lowering LDL-C. The extent of reduction in VLDL particles depended on the baseline level of Lp(a). Our findings suggest a marked effect of evolocumab on VLDL metabolism in subjects with elevated Lp(a).

Trial registration

Clinical trial registration information is registered at ClinicalTrials.gov on April 14, 2016 with the registration number NCT02729025.

Apolipoprotein A-I concentrations and risk of coronary artery disease: A Mendelian randomization study

BACKGROUND AND AIMS

Apolipoprotein A-I (apoA-I) infusions represent a potential novel therapeutic approach for the prevention of coronary artery disease (CAD). Although circulating apoA-I concentrations inversely associate with risk of CAD, the evidence base of this representing a causal relationship is lacking. The aim was to assess the causal role of apoA-I using human genetics.

METHODS

We identified a variant (rs12225230) in APOA1 locus that associated with circulating apoA-I concentrations (p < 5 × 10-8) in 20,370 Finnish participants, and meta-analyzed our data with a previous GWAS of apoA-I. We obtained genetic estimates of CAD from UK Biobank and CARDIoGRAMplusC4D (totaling 122,733 CAD cases) and conducted a two-sample Mendelian randomization analysis. We compared our genetic findings to observational associations of apoA-I with risk of CAD in 918 incident CAD cases among 11,535 individuals from population-based prospective cohorts.

RESULTS

ApoA-I was associated with a lower risk of CAD in observational analyses (HR 0.81; 95%CI: 0.75, 0.88; per 1-SD higher apoA-I), with the association showing a dose-response relationship. Rs12225230 associated with apoA-I concentrations (per-C allele beta 0.076 SD; SE: 0.013; p = 1.5 × 10-9) but not with confounders. In Mendelian randomization analyses, apoA-I was not related to risk of CAD (OR 1.13; 95%CI: 0.98,1.30 per 1-SD higher apoA-I), which was different from the observational association. Similar findings were observed using an independent ABCA1 variant in sensitivity analysis.

CONCLUSIONS

Genetic evidence fails to support a cardioprotective role for apoA-I. This is in line with the cumulative evidence showing that HDL-related phenotypes are unlikely to have a protective role in CAD.