Usefulness of Heart Failure Categories Based on Left Ventricular Ejection Fraction.

BACKGROUND: Heart failure guidelines have recently introduced a narrow category with mildly reduced left ventricular ejection fraction (LVEF) (heart failure with mildly reduced ejection fraction; LVEF 41%-49%) between the previous categories of reduced (heart failure with reduced ejection fraction; LVEF ≤40%) and preserved (heart failure with preserved ejection fraction; LVEF ≥50%) ejection fraction. Grouping of continuous measurements into narrow categories can be questioned if their variability is high. METHODS AND RESULTS: We constructed a cohort of all 9716 new cases of chronic heart failure with an available LVEF in Stockholm, Sweden, from January 1, 2015, until December 31, 2020. All values of LVEF were collected over time, and patients were followed up until death, moving out of Stockholm, or end of study. Mixed models were used to quantify within-person variance in LVEF, and multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. LVEF values followed a normal distribution. The SD of the within-person variance in LVEF over time was 7.4%. The mean time spent in any LVEF category before transition to another category was on average <1 year for heart failure with mildly reduced ejection fraction. Probabilities of transitioning between categories during the first year were substantial; patients with heart failure with mildly reduced ejection fraction had a probability of <25% of remaining in that category 1 year later. CONCLUSIONS: LVEF follows a normal distribution and has considerable variability over time, which may impose a risk for underuse of efficient treatment. The heart failure with mildly reduced ejection fraction category is especially inconstant. Assumptions of a patient's current LVEF should take this variability and the normal distribution of LVEF into account.

Are there lost opportunities in chronic kidney disease? A region-wide cohort study.

OBJECTIVES: Identify the windows of opportunity for the diagnosis of chronic kidney disease (CKD) and the prevention of its adverse outcomes and quantify the potential population gains of such prevention. DESIGN AND SETTING: Observational, population-wide study of residents in the Stockholm and Skåne regions of Sweden between 1 January 2015 and 31 December 2020. PARTICIPANTS: All patients who did not yet have a diagnosis of CKD in healthcare but had CKD according to laboratory measurements of CKD biomarkers available in electronic health records. OUTCOME MEASURES: We assessed the proportions of the patient population that received a subsequent diagnosis of CKD in healthcare, that used guideline-directed pharmacological therapy (statins, renin-angiotensin aldosterone system inhibitors (RAASi) and/or sodium-glucose cotransporter-2 inhibitors (SGLT2i)) and that experienced adverse outcomes (all-cause mortality, cardiovascular mortality or major adverse cardiovascular events (MACE)). The potential to prevent adverse outcomes in CKD was assessed using simulations of guideline-directed pharmacological therapy in untreated subsets of the study population. RESULTS: We identified 99 382 patients with undiagnosed CKD during the study period. Only 33% of those received a subsequent diagnosis of CKD in healthcare after 5 years. The proportion that used statins or RAASi was of similar size to the proportion that didn't, regardless of how advanced their CKD was. The use of SGLT2i was negligible. In simulations of optimal treatment, 22% of the 21 870 deaths, 27% of the 14 310 cardiovascular deaths and 39% of the 22 224 MACE could have been avoided if every patient who did not use an indicated medication for their laboratory-confirmed CKD was treated with guideline-directed pharmacological therapy for CKD. CONCLUSIONS: While we noted underdiagnosis and undertreatment of CKD in this large contemporary population, we also identified a substantial realisable potential to improve CKD outcomes and reduce its burden by treating patients early with guideline-directed pharmacological therapy.

A retrospective nationwide analysis of evolocumab use in Sweden and its effect on low-density lipoprotein cholesterol levels.

Background: Treatment with proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors reduces low-density lipoprotein cholesterol (LDL-C) levels and decreases the incidence of major ischaemic events in clinical trials. However, less is known about the efficacy of PCSK9 inhibition in clinical practice. This study aimed to describe the change in LDL-C levels over time and LDL-C goal achievement in patients with/without atherosclerotic cardiovascular disease (ASCVD), who were prescribed evolocumab in clinical practice, and to describe adherence to and persistence with treatment. Methods: Patients in Sweden with at least one evolocumab prescription filled between July 2015 and May 2020 were included. Medical history and lipid-lowering therapy (LLT) were sourced from national registries. LDL-C levels before and after treatment initiation were assessed using medical records. Persistence with and adherence to evolocumab and oral LLT were assessed up to 12 months after treatment initiation using the refill-gap method and proportion of days covered, respectively. Results: Of the 2,360 patients with at least one prescription for evolocumab, 2,341 were included; 1,858 had ASCVD. Persistence with (76%) and adherence to (86%) evolocumab were high throughout the 12 months following initiation. Mean LDL-C levels decreased by 53% (95% confidence interval [CI]: 51-55%) in patients adherent to evolocumab (n = 567) and 59% (95% CI: 55-63%) in patients adherent to evolocumab and oral LLT (n = 186). Similar reductions in LDL-C were observed in patients with/without ASCVD. Reduced LDL-C levels remained stable during follow-up. Amongst patients adherent to evolocumab and those adherent to evolocumab and oral LLT, 23 and 55% achieved the LDL-C goal of <1.4 mmol/L, respectively. Conclusions: The evolocumab LDL-C-lowering effect observed in clinical trials was confirmed in clinical practice in Sweden, particularly in patients also treated with oral LLT. During follow-up, adherence to and persistence with evolocumab were high, with stable reduced levels of LDL-C during observation.

Heart failure outcomes by left ventricular ejection fraction in a contemporary region-wide patient cohort.

AIMS: This study aimed to characterize a contemporary population with subtypes of incident or prevalent heart failure (HF) based on reduced (HFrEF), mildly reduced, or preserved (HFpEF) left ventricular ejection fraction (LVEF) and to assess how outcomes, healthcare, treatments, and healthcare costs vary between each subtype of incident HF. METHODS AND RESULTS: Using Swedish data from the CardioRenal and Metabolic disease Heart Failure (CaReMe HF) study, updated to cover a more recent time period, this population-based study characterized patients from Stockholm County, Sweden, with incident HF (patients with a first HF diagnosis between 1 January 2015 and 31 December 2019) or prevalent HF (patients with a first HF diagnosis before 1 January 2020). Patients with incident HF had LVEF measured by echocardiography within ±90 days of their first HF diagnosis, and patients with prevalent HF within 5 years prior to the index date. The 13 375 patients with prevalent HF (39.2% women, mean age 73.9 years) had multiple comorbidities (cardiovascular diseases, chronic kidney disease, diabetes, and cancer). These were already highly prevalent at the time of the first HF diagnosis in the 8042 patients with incident HF (40.5% women, mean age 72.3 years). Patients with incident HFpEF received less specialist HF care at outpatient secondary care facilities following their first HF diagnosis than those with incident HFrEF. Patients with HFrEF had higher risks of complications and exerted a higher burden, in terms of care for and costs of HF, on the healthcare system. CONCLUSIONS: This study of contemporary patients with incident HF demonstrates that those with HFpEF and HFrEF differ considerably in terms of clinical presentation, prognosis, and care, highlighting a potential to improve HF outcomes.

Plasma Protein Profiling of Incident Cardiovascular Diseases: A Multisample Evaluation.

BACKGROUND: Proteomic profiling could potentially disclose new pathophysiological pathways for cardiovascular diseases (CVD) and improve prediction at the individual level. We therefore aimed to study the plasma protein profile associated with the incidence of different CVDs. METHODS: Plasma levels of 245 proteins suspected to be linked to CVD or metabolism were measured in 4 Swedish prospective population-based cohorts (SIMPLER [Swedish Infrastructure for Medical Population-Based Life-Course and Environmental Research], ULSAM (Uppsala Longitudinal Study of Adult Men), EpiHealth, and POEM [Prospective Investigation of Obesity, Energy Production, and Metabolism]) comprising 11 869 individuals, free of CVD diagnoses at baseline. Our primary CVD outcome was defined by a combined end point that included either incident myocardial infarction, stroke, or heart failure. RESULTS: Using a discovery/validation approach, 42 proteins were associated with our primary composite end point occurring in 1163 subjects. In separate meta-analyses for each of the 3 CVD outcomes, 49 proteins were related to myocardial infarction, 34 to ischemic stroke, and 109 to heart failure. Thirteen proteins were related to all 3 outcomes. Of those, urokinase plasminogen activator surface receptor, adrenomedullin, and KIM-1 (kidney injury molecule 1) were also related to several markers of subclinical CVD in Prospective Investigation of Obesity, Energy production and Metabolism, reflecting myocardial or arterial pathologies. In prediction analysis, a lasso selection of 11 proteins in ULSAM improved the discrimination of CVD by 3.3% (P<0.0001) in SIMPLER when added to traditional risk factors. CONCLUSIONS: Protein profiling in multiple samples disclosed several new proteins to be associated with subsequent myocardial infarction, stroke, and heart failure, suggesting common pathophysiological pathways for these diseases. KIM-1, urokinase plasminogen activator surface receptor, and adrenomedullin were novel early markers of CVD. A selection of 11 proteins improved the discrimination of CVD.

Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases.

BACKGROUND: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. METHODS: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. RESULTS: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). CONCLUSION: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

AIMS/HYPOTHESIS: The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. METHODS: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). RESULTS: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. CONCLUSIONS/INTERPRETATION: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake.

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.

Single-cell transcriptome dataset of human and mouse in vitro adipogenesis models.

Adipogenesis is a process in which fat-specific progenitor cells (preadipocytes) differentiate into adipocytes that carry out the key metabolic functions of the adipose tissue, including glucose uptake, energy storage, and adipokine secretion. Several cell lines are routinely used to study the molecular regulation of adipogenesis, in particular the immortalized mouse 3T3-L1 line and the primary human Simpson-Golabi-Behmel syndrome (SGBS) line. However, the cell-to-cell variability of transcriptional changes prior to and during adipogenesis in these models is not well understood. Here, we present a single-cell RNA-Sequencing (scRNA-Seq) dataset collected before and during adipogenic differentiation of 3T3-L1 and SGBS cells. To minimize the effects of experimental variation, we mixed 3T3-L1 and SGBS cells and used computational analysis to demultiplex transcriptomes of mouse and human cells. In both models, adipogenesis results in the appearance of three cell clusters, corresponding to preadipocytes, early and mature adipocytes. These data provide a groundwork for comparative studies on these widely used in vitro models of human and mouse adipogenesis, and on cell-to-cell variability during this process.

Single-cell transcriptome dataset of human and mouse in vitro adipogenesis models.

Adipogenesis is a process in which fat-specific progenitor cells (preadipocytes) differentiate into adipocytes that carry out the key metabolic functions of the adipose tissue, including glucose uptake, energy storage, and adipokine secretion. Several cell lines are routinely used to study the molecular regulation of adipogenesis, in particular the immortalized mouse 3T3-L1 line and the primary human Simpson-Golabi-Behmel syndrome (SGBS) line. However, the cell-to-cell variability of transcriptional changes prior to and during adipogenesis in these models is not well understood. Here, we present a single-cell RNA-Sequencing (scRNA-Seq) dataset collected before and during adipogenic differentiation of 3T3-L1 and SGBS cells. To minimize the effects of experimental variation, we mixed 3T3-L1 and SGBS cells and used computational analysis to demultiplex transcriptomes of mouse and human cells. In both models, adipogenesis results in the appearance of three cell clusters, corresponding to preadipocytes, early and mature adipocytes. These data provide a groundwork for comparative studies on human and mouse adipogenesis, as well as on cell-to-cell variability in gene expression during this process.

Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Fibroblast Growth Factor-23 and Risk of Cardiovascular Diseases: A Mendelian Randomization Study.

BACKGROUND: Fibroblast growth factor-23 (FGF-23) is associated with a range of cardiovascular and noncardiovascular diseases in conventional epidemiological studies, but substantial residual confounding may exist. Mendelian randomization approaches can help control for such confounding. METHODS: SCALLOP Consortium data of 19,195 participants were used to generate an FGF-23 genetic score. Data from 337,448 UK Biobank participants were used to estimate associations between higher genetically predicted FGF-23 concentration and the odds of any atherosclerotic cardiovascular disease (n=26,266 events), nonatherosclerotic cardiovascular disease (n=12,652), and noncardiovascular diseases previously linked to FGF-23. Measurements of carotid intima-media thickness and left ventricular mass were available in a subset. Associations with cardiovascular outcomes were also tested in three large case-control consortia: CARDIOGRAMplusC4D (coronary artery disease, n=181,249 cases), MEGASTROKE (stroke, n=34,217), and HERMES (heart failure, n=47,309). RESULTS: We identified 34 independent variants for circulating FGF-23, which formed a validated genetic score. There were no associations between genetically predicted FGF-23 and any of the cardiovascular or noncardiovascular outcomes. In UK Biobank, the odds ratio (OR) for any atherosclerotic cardiovascular disease per 1-SD higher genetically predicted logFGF-23 was 1.03 (95% confidence interval [95% CI], 0.98 to 1.08), and for any nonatherosclerotic cardiovascular disease, it was 1.01 (95% CI, 0.94 to 1.09). The ORs in the case-control consortia were 1.00 (95% CI, 0.97 to 1.03) for coronary artery disease, 1.01 (95% CI, 0.95 to 1.07) for stroke, and 1.00 (95% CI, 0.95 to 1.05) for heart failure. In those with imaging, logFGF-23 was not associated with carotid or cardiac abnormalities. CONCLUSIONS: Genetically predicted FGF-23 levels are not associated with atherosclerotic and nonatherosclerotic cardiovascular diseases, suggesting no important causal link. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_01_10_CJN05080422.mp3.

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients.

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We aimed to train and validate a deep learning model using ECGs to predict myocardial infarction in real-world emergency department patients. We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained because of their presenting complaint. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs to the model were ECG tracing, age, and sex; and outputs were the probabilities of three mutually exclusive classes: non-ST-elevation myocardial infarction (NSTEMI), ST-elevation myocardial infarction (STEMI), and control status, as registered in the SWEDEHEART and other registries. We used an ensemble of five models. Among 492,226 ECGs in 214,250 patients, 5,416 were recorded with an NSTEMI, 1,818 a STEMI, and 485,207 without a myocardial infarction. In a random test set, our model could discriminate STEMIs/NSTEMIs from controls with a C-statistic of 0.991/0.832 and had a Brier score of 0.001/0.008. The model obtained a similar performance in a temporally separated test set of the study sample, and achieved a C-statistic of 0.985 and a Brier score of 0.002 in discriminating STEMIs from controls in an external test set. We developed and validated a deep learning model with excellent performance in discriminating between control, STEMI, and NSTEMI on the presenting ECG of a real-world sample of the important population of all-comers to the emergency department. Hence, deep learning models for ECG decision support could be valuable in the emergency department.

Genetic Landscape of the ACE2 Coronavirus Receptor.

BACKGROUND: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. METHODS: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. RESULTS: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. CONCLUSIONS: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.

A genome-wide association study in a large community-based cohort identifies multiple loci associated with susceptibility to bacterial and viral infections.

There is limited data on host-specific genetic determinants of susceptibility to bacterial and viral infections. Genome-wide association studies using large population cohorts can be a first step towards identifying patients prone to infectious diseases and targets for new therapies. Genetic variants associated with clinically relevant entities of bacterial and viral infections (e.g., abdominal infections, respiratory infections, and sepsis) in 337,484 participants of the UK Biobank cohort were explored by genome-wide association analyses. Cases (n = 81,179) were identified based on ICD-10 diagnosis codes of hospital inpatient and death registries. Functional annotation was performed using gene expression (eQTL) data. Fifty-seven unique genome-wide significant loci were found, many of which are novel in the context of infectious diseases. Some of the detected genetic variants were previously reported associated with infectious, inflammatory, autoimmune, and malignant diseases or key components of the immune system (e.g., white blood cells, cytokines). Fine mapping of the HLA region revealed significant associations with HLA-DQA1, HLA-DRB1, and HLA-DRB4 locus alleles. PPP1R14A showed strong colocalization with abdominal infections and gene expression in sigmoid and transverse colon, suggesting causality. Shared significant loci across infections and non-infectious phenotypes in the UK Biobank cohort were found, suggesting associations for example between SNPs identified for abdominal infections and CRP, rheumatoid arthritis, and diabetes mellitus. We report multiple loci associated with bacterial and viral infections. A better understanding of the genetic determinants of bacterial and viral infections can be useful to identify patients at risk and in the development of new drugs.

Large-Scale Plasma Protein Profiling of Incident Myocardial Infarction, Ischemic Stroke, and Heart Failure.

Background We recently reported a link between plasma levels of 2 of 84 cardiovascular disease (CVD)-related proteins and the 3 major CVDs, myocardial infarction, ischemic stroke, and heart failure. The present study investigated whether measurement of almost 10 times the number of proteins could lead to discovery of additional risk markers for CVD. Methods and Results We measured 742 proteins using the proximity extension assay in 826 male participants of ULSAM (Uppsala Longitudinal Study of Adult Men) who were free from CVD at the age of 70 years. Cox proportional hazards models were adjusted for age only, as well as all traditional risk factors. During a 12.5-year median follow-up (maximal, 22.0 years), 283 incident CVDs occurred. Forty-one proteins were significantly (false discovery rate <0.05) related to the combined end point of incident CVD, with N-terminal pro-brain natriuretic peptide as the top finding, while 53 proteins were related to incident myocardial infarction. A total of 13 and 16 proteins were significantly related to incident ischemic stroke and heart failure, respectively. Growth differentiation factor 15, 4-disulfide core domain protein 2, and kidney injury molecule were related to all of the 3 major CVD outcomes. A lasso selection of 11 proteins improved discrimination of incident CVD by 5.0% (P=0.0038). Conclusions Large-scale proteomics seem useful for the discovery of new risk markers for CVD and to improve risk prediction in an elderly population of men. Further studies are needed to replicate the findings in independent samples of both men and women of different ages.

Renin-Angiotensin Aldosterone System Inhibitors in Primary Prevention and COVID-19.

Background Considering the widespread risk of collider bias and confounding by indication in previous research, the associations between renin-angiotensin aldosterone system (RAAS) inhibitor use and COVID-19 remain unknown. Accordingly, this study tested the hypothesis that RAAS inhibitors influence the summation effect of COVID-19 and its progression to severe outcomes. Methods and Results This nationwide cohort study compared all residents of Sweden, without prior cardiovascular disease, in monotherapy (as of January 1, 2020) with a RAAS inhibitor to those using a calcium channel blocker or a thiazide diuretic. Comparative cohorts were balanced using machine-learning-derived propensity score methods. Of 165 355 people in the analysis (51% women), 367 were hospitalized or died with COVID-19 (246 using a RAAS inhibitor versus 121 using a calcium channel blocker or thiazide diuretic; Cox proportional hazard ratio [HR], 0.97; 95% CI, 0.74-1.27). When each outcome was assessed separately, 335 people were hospitalized with COVID-19 (HR, 0.92; 95% CI, 0.70-1.22), and 64 died with COVID-19 (HR, 1.22; 95% CI, 0.68-2.19). The severity of COVID-19 outcomes did not differ between those using a RAAS inhibitor and those using a calcium channel blocker or thiazide diuretic (ordered logistic regression odds ratio, 1.01; 95% CI, 0.89-1.14). Conclusions Despite potential limitations, this study is among the best available evidence that RAAS inhibitor use in primary prevention does not increase the risk of severe COVID-19 outcomes; presenting strong data from which scientists and policy makers alike can base, with greater confidence, their current position on the safety of using RAAS inhibitors during the COVID-19 pandemic.

A multi-ethnic epigenome-wide association study of leukocyte DNA methylation and blood lipids.

Here we examine the association between DNA methylation in circulating leukocytes and blood lipids in a multi-ethnic sample of 16,265 subjects. We identify 148, 35, and 4 novel associations among Europeans, African Americans, and Hispanics, respectively, and an additional 186 novel associations through a trans-ethnic meta-analysis. We observe a high concordance in the direction of effects across racial/ethnic groups, a high correlation of effect sizes between high-density lipoprotein and triglycerides, a modest overlap of associations with epigenome-wide association studies of other cardio-metabolic traits, and a largely non-overlap with lipid loci identified to date through genome-wide association studies. Thirty CpGs reached significance in at least 2 racial/ethnic groups including 7 that showed association with the expression of an annotated gene. CpGs annotated to CPT1A showed evidence of being influenced by triglycerides levels. DNA methylation levels of circulating leukocytes show robust and consistent association with blood lipid levels across multiple racial/ethnic groups.