Complex effects of sequence variants on lipid levels and coronary artery disease.

Many sequence variants have additive effects on blood lipid levels and, through that, on the risk of coronary artery disease (CAD). We show that variants also have non-additive effects and interact to affect lipid levels as well as affecting variance and correlations. Variance and correlation effects are often signatures of epistasis or gene-environmental interactions. These complex effects can translate into CAD risk. For example, Trp154Ter in FUT2 protects against CAD among subjects with the A1 blood group, whereas it associates with greater risk of CAD in others. His48Arg in ADH1B interacts with alcohol consumption to affect lipid levels and CAD. The effect of variants in TM6SF2 on blood lipids is greatest among those who never eat oily fish but absent from those who often do. This work demonstrates that variants that affect variance of quantitative traits can allow for the discovery of epistasis and interactions of variants with the environment.

The Born in Guangzhou Cohort Study enables generational genetic discoveries.

Genomic research that targets large-scale, prospective birth cohorts constitutes an essential strategy for understanding the influence of genetics and environment on human health1. Nonetheless, such studies remain scarce, particularly in Asia. Here we present the phase I genome study of the Born in Guangzhou Cohort Study2 (BIGCS), which encompasses the sequencing and analysis of 4,053 Chinese individuals, primarily composed of trios or mother-infant duos residing in South China. Our analysis reveals novel genetic variants, a high-quality reference panel, and fine-scale local genetic structure within BIGCS. Notably, we identify previously unreported East Asian-specific genetic associations with maternal total bile acid, gestational weight gain and infant cord blood traits. Additionally, we observe prevalent age-specific genetic effects on lipid levels in mothers and infants. In an exploratory intergenerational Mendelian randomization analysis, we estimate the maternal putatively causal and fetal genetic effects of seven adult phenotypes on seven fetal growth-related measurements. These findings illuminate the genetic links between maternal and early-life traits in an East Asian population and lay the groundwork for future research into the intricate interplay of genetics, intrauterine exposures and early-life experiences in shaping long-term health.

Quasi-experimental evaluation of a nationwide diabetes prevention programme.

Diabetes is a leading cause of morbidity, mortality and cost of illness1,2. Health behaviours, particularly those related to nutrition and physical activity, play a key role in the development of type 2 diabetes mellitus3. Whereas behaviour change programmes (also known as lifestyle interventions or similar) have been found efficacious in controlled clinical trials4,5, there remains controversy about whether targeting health behaviours at the individual level is an effective preventive strategy for type 2 diabetes mellitus6 and doubt among clinicians that lifestyle advice and counselling provided in the routine health system can achieve improvements in health7-9. Here we show that being referred to the largest behaviour change programme for prediabetes globally (the English Diabetes Prevention Programme) is effective in improving key cardiovascular risk factors, including glycated haemoglobin (HbA1c), excess body weight and serum lipid levels. We do so by using a regression discontinuity design10, which uses the eligibility threshold in HbA1c for referral to the behaviour change programme, in electronic health data from about one-fifth of all primary care practices in England. We confirm our main finding, the improvement of HbA1c, using two other quasi-experimental approaches: difference-in-differences analysis exploiting the phased roll-out of the programme and instrumental variable estimation exploiting regional variation in programme coverage. This analysis provides causal, rather than associational, evidence that lifestyle advice and counselling implemented at scale in a national health system can achieve important health improvements.

Inhibition of ASGR1 decreases lipid levels by promoting cholesterol excretion.

High cholesterol is a major risk factor for cardiovascular disease1. Currently, no drug lowers cholesterol through directly promoting cholesterol excretion. Human genetic studies have identified that the loss-of-function Asialoglycoprotein receptor 1 (ASGR1) variants associate with low cholesterol and a reduced risk of cardiovascular disease2. ASGR1 is exclusively expressed in liver and mediates internalization and lysosomal degradation of blood asialoglycoproteins3. The mechanism by which ASGR1 affects cholesterol metabolism is unknown. Here, we find that Asgr1 deficiency decreases lipid levels in serum and liver by stabilizing LXRα. LXRα upregulates ABCA1 and ABCG5/G8, which promotes cholesterol transport to high-density lipoprotein and excretion to bile and faeces4, respectively. ASGR1 deficiency blocks endocytosis and lysosomal degradation of glycoproteins, reduces amino-acid levels in lysosomes, and thereby inhibits mTORC1 and activates AMPK. On one hand, AMPK increases LXRα by decreasing its ubiquitin ligases BRCA1/BARD1. On the other hand, AMPK suppresses SREBP1 that controls lipogenesis. Anti-ASGR1 neutralizing antibody lowers lipid levels by increasing cholesterol excretion, and shows synergistic beneficial effects with atorvastatin or ezetimibe, two widely used hypocholesterolaemic drugs. In summary, this study demonstrates that targeting ASGR1 upregulates LXRα, ABCA1 and ABCG5/G8, inhibits SREBP1 and lipogenesis, and therefore promotes cholesterol excretion and decreases lipid levels.

A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics.

Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.

Polygenic scores for complex traits are associated with changes in concentration of circulating lipid species.

Understanding perturbations in circulating lipid levels that often occur years or decades before clinical symptoms may enhance our understanding of disease mechanisms and provide novel intervention opportunities. Here, we assessed if polygenic scores (PGSs) for complex traits could detect lipid dysfunctions related to the traits and provide new biological insights. We constructed genome-wide PGSs (approximately 1 million genetic variants) for 50 complex traits in 7,169 Finnish individuals with routine clinical lipid profiles and lipidomics measurements (179 lipid species). We identified 678 associations (P < 9.0 × 10-5) involving 26 traits and 142 lipids. Most of these associations were also validated with the actual phenotype measurements where available (89.5% of 181 associations where the trait was available), suggesting that these associations represent early signs of physiological changes of the traits. We detected many known relationships (e.g., PGS for body mass index (BMI) and lysophospholipids, PGS for type 2 diabetes and triacyglycerols) and those that suggested potential target for prevention strategies (e.g., PGS for venous thromboembolism and arachidonic acid). We also found association of PGS for favorable adiposity with increased sphingomyelins levels, suggesting a probable role of sphingomyelins in increased risk for certain disease, e.g., venous thromboembolism as reported previously, in favorable adiposity despite its favorable metabolic effect. Altogether, our study provides a comprehensive characterization of lipidomic alterations in genetic predisposition for a wide range of complex traits. The study also demonstrates potential of PGSs for complex traits to capture early, presymptomatic lipid alterations, highlighting its utility in understanding disease mechanisms and early disease detection.

Genetic Architecture and Clinical Outcomes of Combined Lipid Disturbances.

BACKGROUND: Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort. METHODS: Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes. Incident coronary artery disease risk was estimated for each subtype using Cox proportional hazards models. Phenome-wide analyses and genome-wide association studies were performed for each subtype, followed by in silico causal gene prioritization and heritability analyses. Additionally, the prevalence of disruptive variants in causal genes for Mendelian lipid disorders was assessed using whole-exome sequence data. RESULTS: Among 450 636 UK Biobank participants: 63 (0.01%) had chylomicronemia; 40 005 (8.9%) had hypercholesterolemia; 94 785 (21.0%) had combined hyperlipidemia; 13 998 (3.1%) had remnant hypercholesterolemia; 110 389 (24.5%) had hypertriglyceridemia; and 49 (0.01%) had mixed hypertriglyceridemia and hypercholesterolemia. Over a median (interquartile range) follow-up of 11.1 (10.4-11.8) years, incident coronary artery disease risk varied across subtypes, with combined hyperlipidemia exhibiting the largest hazard (hazard ratio, 1.92 [95% CI, 1.84-2.01]; P=2×10-16), even when accounting for non-HDL-C (hazard ratio, 1.45 [95% CI, 1.30-1.60]; P=2.6×10-12). Genome-wide association studies revealed 250 loci significantly associated with dyslipoproteinemia subtypes, of which 72 (28.8%) were not detected in prior single lipid trait genome-wide association studies. Mendelian lipid variant carriers were rare (2.0%) among individuals with dyslipoproteinemia, but polygenic heritability was high, ranging from 23% for remnant hypercholesterolemia to 54% for combined hyperlipidemia. CONCLUSIONS: Simultaneous assessment of multiple lipid derangements revealed nuanced differences in coronary artery disease risk and genetic architectures across dyslipoproteinemia subtypes. These findings highlight the importance of looking beyond single lipid traits to better understand combined lipid and lipoprotein phenotypes and implications for disease risk.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study.

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

One-Year Effects of High-Intensity Statin on Bioactive Lipids: Findings From the JUPITER Trial.

BACKGROUND: Statin effects extend beyond low-density lipoprotein cholesterol reduction, potentially modulating the metabolism of bioactive lipids (BALs), crucial for biological signaling and inflammation. These bioactive metabolites may serve as metabolic footprints, helping uncover underlying processes linked to pleiotropic effects of statins and yielding a better understanding of their cardioprotective properties. This study aimed to investigate the impact of high-intensity statin therapy versus placebo on plasma BALs in the JUPITER trial (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; NCT00239681), a randomized primary prevention trial involving individuals with low-density lipoprotein cholesterol <130 mg/dL and high-sensitivity C-reactive protein ≥2 mg/L. METHODS: Using a nontargeted mass spectrometry approach, over 11 000 lipid features were assayed from baseline and 1-year plasma samples from cardiovascular disease noncases from 2 nonoverlapping nested substudies: JUPITERdiscovery (n=589) and JUPITERvalidation (n=409). The effect of randomized allocation of rosuvastatin 20 mg versus placebo on BALs was examined by fitting a linear regression with delta values (∆=year 1-baseline) adjusted for age and baseline levels of each feature. Significant associations in discovery were analyzed in the validation cohort. Multiple comparisons were adjusted using 2-stage overall false discovery rate. RESULTS: We identified 610 lipid features associated with statin randomization with significant replication (overall false discovery rate, <0.05), including 26 with annotations. Statin therapy significantly increased levels of 276 features, including BALs with anti-inflammatory activity and arterial vasodilation properties. Concurrently, 334 features were significantly lowered by statin therapy, including arachidonic acid and proinflammatory and proplatelet aggregation BALs. By contrast, statin therapy reduced an eicosapentaenoic acid-derived hydroxyeicosapentaenoic acid metabolite, which may be related to impaired glucose metabolism. Additionally, we observed sex-related differences in 6 lipid metabolites and 6 unknown features. CONCLUSIONS: Statin allocation was significantly associated with upregulation of BALs with anti-inflammatory, antiplatelet aggregation and antioxidant properties and downregulation of BALs with proinflammatory and proplatelet aggregation activity, supporting the pleiotropic effects of statins beyond low-density lipoprotein cholesterol reduction.

Time-Restricted Feeding Reduces Atherosclerosis in LDLR KO Mice but Not in ApoE Knockout Mice.

BACKGROUND: Dyslipidemia increases cardiovascular disease risk, the leading cause of death worldwide. Under time-restricted feeding (TRF), wherein food intake is restricted to a consistent window of <12 hours, weight gain, glucose intolerance, inflammation, dyslipidemia, and hypercholesterolemia are all reduced in mice fed an obesogenic diet. LDLR (low-density lipoprotein receptor) mutations are a major cause of familial hypercholesterolemia and early-onset cardiovascular disease. METHODS: We subjected benchmark preclinical models, mice lacking LDLR-knockout or ApoE knockout to ad libitum feeding of an isocaloric atherogenic diet either ad libitum or 9 hours TRF for up to 13 weeks and assessed disease development, mechanism, and global changes in hepatic gene expression and plasma lipids. In a regression model, a subset of LDLR-knockout mice were ad libitum fed and then subject to TRF. RESULTS: TRF could significantly attenuate weight gain, hypercholesterolemia, and atherosclerosis in mice lacking the LDLR-knockout mice under experimental conditions of both prevention and regression. In LDLR-knockout mice, increased hepatic expression of genes mediating ß-oxidation during fasting is associated with reduced VLDL (very-low-density lipoprotein) secretion and lipid accumulation. Additionally, increased sterol catabolism coupled with fecal loss of cholesterol and bile acids contributes to the atheroprotective effect of TRF. Finally, TRF alone or combined with a cholesterol-free diet can reduce atherosclerosis in LDLR-knockout mice. However, mice lacking ApoE, which is an important protein for hepatic lipoprotein reuptake do not respond to TRF. CONCLUSIONS: In a preclinical animal model, TRF is effective in both the prevention and regression of atherosclerosis in LDLR knockout mice. The results suggest TRF alone or in combination with a low-cholesterol diet can be a lifestyle intervention for reducing cardiovascular disease risk in humans.

An integrative systems genetic analysis of mammalian lipid metabolism.

Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9 as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.

Lipid metabolism during pregnancy: consequences for mother and child.

PURPOSE OF REVIEW: Accommodating fetal growth and development, women undergo multiple physiological changes during pregnancy. In recent years, several studies contributed to the accumulating evidence about the impact of gestational hyperlipidemia on cardiovascular risk for mother and child. This review aims to provide a comprehensive overview of the current research on lipid profile alterations during pregnancy and its associated (cardiovascular) outcomes for mother and child from a clinical perspective. RECENT FINDINGS: In a normal pregnancy, total and LDL-cholesterol levels increase by approximately 30-50%, HDL-cholesterol by 20-40%, and triglycerides by 50-100%. In some women, for example, with familial hypercholesterolemia (FH), a more atherogenic lipid profile is observed. Dyslipidemia during pregnancy is found to be associated with adverse (cardiovascular) outcomes for the mother (e.g. preeclampsia, gestational diabetes, metabolic syndrome, unfavorable lipid profile) and for the child (e.g. preterm birth, large for gestational age, preatherosclerotic lesions, unfavorable lipid profile). SUMMARY: The lipid profile of women during pregnancy provides a unique window of opportunity into the potential future cardiovascular risk for mother and child. Better knowledge about adverse outcomes and specific risk groups could lead to better risk assessment and earlier cardiovascular prevention. Future research should investigate implementation of gestational screening possibilities.

The metabolic signature of blood lipids: a causal inference study using twins.

Dyslipidemia is one of the cardiometabolic risk factors that influences mortality globally. Unraveling the causality between blood lipids and metabolites and the complex networks connecting lipids, metabolites, and other cardiometabolic traits can help to more accurately reflect the body's metabolic disorders and even cardiometabolic diseases. We conducted targeted metabolomics of 248 metabolites in 437 twins from the Chinese National Twin Registry. Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) analysis was used for causal inference between metabolites and lipid parameters. Bidirectional mediation analysis was performed to explore the linkages between blood lipids, metabolites, and other seven cardiometabolic traits. We identified 44, 1, and 31 metabolites associated with triglyceride (TG), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C), most of which were gut microbiota-derived metabolites. There were 9, 1, and 14 metabolites that showed novel associations with TG, TC, and HDL-C, respectively. ICE FALCON analysis found that TG and HDL-C may have a predicted causal effect on 23 and six metabolites, respectively, and one metabolite may have a predicted causal effect on TG. Mediation analysis discovered 14 linkages connecting blood lipids, metabolites, and other cardiometabolic traits. Our study highlights the significance of gut microbiota-derived metabolites in lipid metabolism. Most of the identified cross-sectional associations may be due to the lipids having a predicted causal effect on metabolites, but not vice versa, nor are they due to family confounding. These findings shed new light on lipid metabolism and personalized management of cardiometabolic diseases.

Causal association of blood lipids with all-cause and cause-specific mortality risk: a Mendelian randomization study.

Dyslipidemia has long been implicated in elevating mortality risk; yet, the precise associations between lipid traits and mortality remained undisclosed. Our study aimed to explore the causal effects of lipid traits on both all-cause and cause-specific mortality. One-sample Mendelian randomization (MR) with linear and nonlinear assumptions was conducted in a cohort of 407,951 European participants from the UK Biobank. Six lipid traits, consisting of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and lipoprotein(a), were included to investigate the causal associations with mortality. Two-sample MR was performed to replicate the association between each lipid trait and all-cause mortality. Univariable MR results showed that genetically predicted higher ApoA1 was significantly associated with a decreased all-cause mortality risk (HR[95% CI]:0.93 [0.89-0.97], P value = 0.001), which was validated by the two-sample MR analysis. Higher lipoprotein(a) was associated with an increased risk of all-cause mortality (1.03 [1.01-1.04], P value = 0.002). Multivariable MR confirmed the direct causal effects of ApoA1 and lipoprotein(a) on all-cause mortality. Meanwhile, nonlinear MR found no evidence for nonlinearity between lipids and all-cause mortality. Our examination into cause-specific mortality revealed a suggestive inverse association between ApoA1 and cancer mortality, a significant positive association between lipoprotein(a) and cardiovascular disease mortality, and a suggestive positive association between lipoprotein(a) and digestive disease mortality. High LDL-C was associated with an increased risk of cardiovascular disease mortality but a decreased risk of neurodegenerative disease mortality. The findings suggest that implementing interventions to raise ApoA1 and decrease lipoprotein(a) levels may improve overall health outcomes and mitigate cancer and digestive disease mortality.

Automated preparation of plasma lipids, metabolites, and proteins for LC/MS-based analysis of a high-fat diet in mice.

Blood plasma is one of the most commonly analyzed and easily accessible biological samples. Here, we describe an automated liquid-liquid extraction platform that generates accurate, precise, and reproducible samples for metabolomic, lipidomic, and proteomic analyses from a single aliquot of plasma while minimizing hands-on time and avoiding contamination from plasticware. We applied mass spectrometry to examine the metabolome, lipidome, and proteome of 90 plasma samples to determine the effects of age, time of day, and a high-fat diet in mice. From 25 µl of mouse plasma, we identified 907 lipid species from 16 different lipid classes and subclasses, 233 polar metabolites, and 344 proteins. We found that the high-fat diet induced only mild changes in the polar metabolome, upregulated apolipoproteins, and induced substantial shifts in the lipidome, including a significant increase in arachidonic acid and a decrease in eicosapentaenoic acid content across all lipid classes.

Effect of IL-6R blockade on plasma lipids and clinical outcomes among hospitalized patients with COVID-19 infection.

Plasma lipid levels are modulated by systemic infection and inflammation; it is unknown whether these changes reflect inflammatory responses or caused directly by pathogen presence. We explored the hypothesis that anti-inflammatory intervention via interleukin 6 receptor (IL-6R) blockade would influence plasma lipid levels during severe infection and evaluated the association of plasma lipid changes with clinical outcomes. Sarilumab (monoclonal antibody blocking IL-6R) efficacy was previously assessed in patients with coronavirus disease 2019 (COVID-19) (NCT04315298). This analysis determined whether strong inflammatory reduction by sarilumab in patients with COVID-19 pneumonia of increasing severity (severe, critical, multisystem organ dysfunction) affected plasma lipid changes between day 1 and day 7 of study therapy. Baseline lipid levels reflected the presence of acute systemic infection, characterized by very low HDL-C, low LDL-C, and moderately elevated triglycerides (TGs). Disease severity was associated with progressively more abnormal lipid levels. At day 7, median lipid levels increased more in the sarilumab versus placebo group (HDL-C +10.3%, LDL-C +54.7%, TG +32% vs. HDL-C +1.7%, LDL-C +15.4%, TG +8.8%, respectively). No significant association between lipid changes and clinical outcomes was observed. In conclusion, severe-to-critical COVID-19 pneumonia causes profound HDL-C depression that is only modestly responsive to strong anti-IL-6R inflammatory intervention. Conversely, LDL-C depression is strongly responsive to IL-6R blockade, with LDL-C levels likely returning to the predisease set point. These results advance our understanding of the complex relationship between serum lipids and infection/inflammation and suggest that HDL-C depression during acute contagious disease is driven by infection and not IL-6-mediated inflammation.

siRNA nanoparticle targeting Usp20 lowers lipid levels and ameliorates metabolic syndrome in mice.

Atherosclerotic cardiovascular disease is closely correlated with elevated low density lipoprotein-cholesterol. In feeding state, glucose and insulin activate mammalian target of rapamycin 1 that phosphorylates the deubiquitylase ubiquitin-specific peptidase 20 (USP20). USP20 then stabilizes HMG-CoA reductase, thereby increasing lipid biosynthesis. In this study, we applied clinically approved lipid nanoparticles to encapsulate the siRNA targeting Usp20. We demonstrated that silencing of hepatic Usp20 by siRNA decreased body weight, improved insulin sensitivity, and increased energy expenditure through elevating UCP1. In Ldlr-/- mice, silencing Usp20 by siRNA decreased lipid levels and prevented atherosclerosis. This study suggests that the RNAi-based therapy targeting hepatic Usp20 has a translational potential to treat metabolic disease.

Comparing the Extraction Performance in Mouse Plasma of Different Biphasic Methods for Polar and Nonpolar Compounds.

Many metabolomic studies are interested in both polar and nonpolar analyses. However, the available sample volume often precludes multiple separate extractions. Therefore, there are major advantages in performing a biphasic extraction and retaining both phases for subsequent separate analyses. To be successful, such approaches require the method to be robust and repeatable for both phases. Hence, we determined the performance of three extraction protocols, plus two variant versions, using 25 µL of commercially available mouse plasma. The preferred option for nonpolar lipids was a modified diluted version of a method employing methyl tert-butyl ether (MTBE) suggested by Matyash and colleagues due to its high repeatability for nonpolar compounds. For polar compounds, the Bligh-Dyer method performs best for sensitivity but with consequentially poorer lipid performance. Overall, the scaled-down version of the MTBE method gave the best overall performance, with high sensitivity for both polar and nonpolar compounds and good repeatability for polar compounds in particular.