Biomarker and transcriptomics profiles of serum selenium concentrations in patients with heart failure are associated with immunoregulatory processes.

BACKGROUND: Low selenium concentrations are associated with worse outcomes in heart failure (HF). However, the underlying pathophysiologic mechanisms remain incompletely understood. Therefore, we aimed to contrast serum selenium concentrations to blood biomarker and transcriptomic profiles in patients with HF. METHODS: Circulating biomarkers, whole blood transcriptomics and serum selenium measurements in a cohort of 2328 patients with HF were utilized. Penalized linear regression and gene expression analysis were used to assess biomarker and transcriptomics profiles, respectively. As a proof-of-principle, potential causal effects of selenium on excreted cytokines concentrations were investigated using human peripheral blood mononuclear cells (PBMCs). RESULTS: Mean selenium levels were 60.6 µg/L in Q1 and 122.0 µg/L in Q4. From 356 biomarkers and 20 clinical features, the penalized linear regression model yielded 44 variables with <5 % marginal false discovery rate as predictors of serum selenium. Biomarkers associated positively with selenium concentrations included: epidermal growth factor receptor (EGFR), IFN-gamma-R1, CD4, GDF15, and IL10. Biomarkers associated negatively with selenium concentrations included: PCSK9, TNFSF13, FGF21 and PAI. Additionally, 148 RNA transcripts were found differentially expressed between high and low selenium status (Padj.<0.05; log-fold-change<|0.25|). Enrichment analyses of the selected biomarkers and RNA transcripts identified similar enriched processes, including regulation processes of leukocyte differentiation and activation, as well as cytokines production. The mRNA expression of two selenoproteins (MSRB1 and GPX4) were strongly correlated with serum selenium, while GPX4, SELENOK, and SELENOS were associated with prognosis. In the in-vitro setting, PBMCs supplemented with selenium showed significantly lower abundance of several (pro-)inflammatory cytokines. CONCLUSION: These data suggest that immunoregulation is an important mechanism through which selenium might have beneficial roles in HF. The beneficial effects of higher serum selenium concentrations are likely because of global immunomodulatory effects on the abundance of cytokines. MSRB1 and GPX4 are potential modulators of and should be pursued in future research.

Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure.

BACKGROUND: Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies. OBJECTIVES: We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death. METHODS: We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients. RESULTS: The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro-B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients. CONCLUSIONS: A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.

Whole blood transcriptomic profiling identifies molecular pathways related to cardiovascular mortality in heart failure.

AIMS: Chronic heart failure (CHF) is a systemic syndrome with a poor prognosis and a need for novel therapies. We investigated whether whole blood transcriptomic profiling can provide new mechanistic insights into cardiovascular (CV) mortality in CHF. METHODS AND RESULTS: Transcriptome profiles were generated at baseline from 944 CHF patients from the BIOSTAT-CHF study, of whom 626 survived and 318 died from a CV cause during a follow-up of 21 months. Multivariable analysis, including adjustment for cell count, identified 1153 genes (6.5%) that were differentially expressed between those that survived or died and strongly related to a validated clinical risk score for adverse prognosis. The differentially expressed genes mainly belonged to five non-redundant pathways: adaptive immune response, proteasome-mediated ubiquitin-dependent protein catabolic process, T-cell co-stimulation, positive regulation of T-cell proliferation, and erythrocyte development. These five pathways were selectively related (RV coefficients >0.20) with seven circulating protein biomarkers of CV mortality (fibroblast growth factor 23, soluble ST2, adrenomedullin, hepcidin, pentraxin-3, WAP 4-disulfide core domain 2, and interleukin-6) revealing an intricate relationship between immune and iron homeostasis. The pattern of survival-associated gene expression matched with 29 perturbagen-induced transcriptome signatures in the iLINCS drug-repurposing database, identifying drugs, approved for other clinical indications, that were able to reverse in vitro the molecular changes associated with adverse prognosis in CHF. CONCLUSION: Systematic modelling of the whole blood protein-coding transcriptome defined molecular pathways that provide a link between clinical risk factors and adverse CV prognosis in CHF, identifying both established and new potential therapeutic targets.

Clinical implications of low estimated protein intake in patients with heart failure.

BACKGROUND: A higher protein intake has been associated with a higher muscle mass and lower mortality rates in the general population, but data about protein intake and survival in patients with heart failure (HF) are lacking. METHODS: We studied the prevalence, predictors, and clinical outcome of estimated protein intake in 2516 patients from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) index cohort. Protein intake was calculated in spot urine samples using a validated formula [13.9 + 0.907 * body mass index (BMI) (kg/m2 ) + 0.0305 * urinary urea nitrogen level (mg/dL)]. Association with mortality was assessed using multivariable Cox regression models. All findings were validated in an independent cohort. RESULTS: We included 2282 HF patients (mean age 68 ± 12 years and 27% female). Lower estimated protein intake in HF patients was associated with a lower BMI, but with more signs of congestion. Mortality rate in the lowest quartile was 32%, compared with 18% in the highest quartile (P < 0.001). In a multivariable model, lower estimated protein intake was associated with a higher risk of death compared with the highest quartile [hazard ratio (HR) 1.50; 95% confidence interval (CI) 1.03-2.18, P = 0.036 for the lowest quartile and HR 1.46; 95% CI 1.00-2.18, P = 0.049 for the second quartile]. CONCLUSIONS: An estimated lower protein intake was associated with a lower BMI, but signs of congestion were more prevalent. A lower estimated protein intake was independently associated with a higher mortality risk.

Telomere length is independently associated with all-cause mortality in chronic heart failure.

OBJECTIVE: Patients with heart failure have shorter mean leucocyte telomere length (LTL), a marker of biological age, compared with healthy subjects, but it is unclear whether this is of prognostic significance. We therefore sought to determine whether LTL is associated with outcomes in patients with heart failure. METHODS: We measured LTL in patients with heart failure from the BIOSTAT-CHF Index (n=2260) and BIOSTAT-CHF Tayside (n=1413) cohorts. Cox proportional hazards analyses were performed individually in each cohort and the estimates combined using meta-analysis. Our co-primary endpoints were all-cause mortality and heart failure hospitalisation. RESULTS: In age-adjusted and sex-adjusted analyses, shorter LTL was associated with higher all-cause mortality in both cohorts individually and when combined (meta-analysis HR (per SD decrease in LTL)=1.16 (95% CI 1.08 to 1.24); p=2.66×10-5), an effect equivalent to that of being four years older. The association remained significant after adjustment for the BIOSTAT-CHF clinical risk score to account for known prognostic factors (HR=1.12 (95% CI 1.05 to 1.20); p=1.04×10-3). Shorter LTL was associated with both cardiovascular (HR=1.09 (95% CI 1.00 to 1.19); p=0.047) and non-cardiovascular deaths (HR=1.18 (95% CI 1.05 to 1.32); p=4.80×10-3). There was no association between LTL and heart failure hospitalisation (HR=0.99 (95% CI 0.92 to 1.07); p=0.855). CONCLUSION: In patients with heart failure, shorter mean LTL is independently associated with all-cause mortality.

A network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic heart failure.

AIMS: Heart failure (HF) is frequently caused by an ischaemic event (e.g. myocardial infarction) but might also be caused by a primary disease of the myocardium (cardiomyopathy). In order to identify targeted therapies specific for either ischaemic or non-ischaemic HF, it is important to better understand differences in underlying molecular mechanisms. METHODS AND RESULTS: We performed a biological physical protein-protein interaction network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic HF. First, differentially expressed plasma protein biomarkers were identified in 1160 patients enrolled in the BIOSTAT-CHF study, 715 of whom had ischaemic HF and 445 had non-ischaemic HF. Second, we constructed an enriched physical protein-protein interaction network, followed by a pathway over-representation analysis. Finally, we identified key network proteins. Data were validated in an independent HF cohort comprised of 765 ischaemic and 100 non-ischaemic HF patients. We found 21/92 proteins to be up-regulated and 2/92 down-regulated in ischaemic relative to non-ischaemic HF patients. An enriched network of 18 proteins that were specific for ischaemic heart disease yielded six pathways, which are related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. We identified five key network proteins: acid phosphatase 5, epidermal growth factor receptor, insulin-like growth factor binding protein-1, plasminogen activator urokinase receptor, and secreted phosphoprotein 1. Similar results were observed in the independent validation cohort. CONCLUSIONS: Pathophysiological pathways distinguishing patients with ischaemic HF from those with non-ischaemic HF were related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. The five key pathway proteins identified are potential treatment targets specifically for patients with ischaemic HF.

Genetic risk and atrial fibrillation in patients with heart failure.

AIMS: To study the association between an atrial fibrillation (AF) genetic risk score with prevalent AF and all-cause mortality in patients with heart failure. METHODS AND RESULTS: An AF genetic risk score was calculated in 3759 European ancestry individuals (1783 with sinus rhythm, 1976 with AF) from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) by summing 97 single nucleotide polymorphism (SNP) alleles (ranging from 0-2) weighted by the natural logarithm of the relative SNP risk from the latest AF genome-wide association study. Further, we assessed AF risk variance explained by additive SNP variation, and performance of clinical or genetic risk factors, and the combination in classifying AF prevalence. AF was classified as AF or atrial flutter (AFL) at baseline electrocardiogram and/or a history of AF or AFL. The genetic risk score was associated with AF after multivariable adjustment. Odds ratio for AF prevalence per 1-unit increase genetic risk score was 2.12 (95% confidence interval 1.84-2.45, P = 2.15 × 10-24 ) in the total cohort, 2.08 (1.72-2.50, P = 1.30 × 10-14 ) in heart failure with reduced ejection fraction (HFrEF) and 2.02 (1.37-2.99, P = 4.37 × 10-4 ) in heart failure with preserved ejection fraction (HFpEF). AF-associated loci explained 22.9% of overall AF SNP heritability. Addition of the genetic risk score to clinical risk factors increased the C-index by 2.2% to 0.721. CONCLUSIONS: The AF genetic risk score was associated with increased AF prevalence in HFrEF and HFpEF. Genetic variation accounted for 22.9% of overall AF SNP heritability. Addition of genetic risk to clinical risk improved model performance in classifying AF prevalence.

Bio-adrenomedullin as a marker of congestion in patients with new-onset and worsening heart failure.

BACKGROUND: Secretion of adrenomedullin (ADM) is stimulated by volume overload to maintain endothelial barrier function, and higher levels of biologically active (bio-) ADM in heart failure (HF) are a counteracting response to vascular leakage and tissue oedema. This study aimed to establish the value of plasma bio-ADM as a marker of congestion in patients with worsening HF. METHODS AND RESULTS: The association of plasma bio-ADM with clinical markers of congestion, as well as its prognostic value was studied in 2179 patients with new-onset or worsening HF enrolled in BIOSTAT-CHF. Data were validated in a separate cohort of 1703 patients. Patients with higher plasma bio-ADM levels were older, had more severe HF and more signs and symptoms of congestion (all P < 0.001). Amongst 20 biomarkers, bio-ADM was the strongest predictor of a clinical congestion score (r2 = 0.198). In multivariable regression analysis, higher bio-ADM was associated with higher body mass index, more oedema, and higher fibroblast growth factor 23. In hierarchical cluster analysis, bio-ADM clustered with oedema, orthopnoea, rales, hepatomegaly and jugular venous pressure. Higher bio-ADM was independently associated with impaired up-titration of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers after 3 months, but not of beta-blockers. Higher bio-ADM levels were independently associated with an increased risk of all-cause mortality and HF hospitalization (hazard ratio 1.16, 95% confidence interval 1.06-1.27, P = 0.002, per log increase). Analyses in the validation cohort yielded comparable findings. CONCLUSIONS: Plasma bio-ADM in patients with new-onset and worsening HF is associated with more severe HF and more oedema, orthopnoea, hepatomegaly and jugular venous pressure. We therefore postulate bio-ADM as a congestion marker, which might become useful to guide decongestive therapy.

Proteomic diversity of high-density lipoprotein explains its association with clinical outcome in patients with heart failure.

AIMS: Previously, low high-density lipoprotein (HDL) cholesterol was found to be one of the strongest predictors of mortality and/or heart failure (HF) hospitalisation in patients with HF. We therefore performed in-depth investigation of the multifunctional HDL proteome to reveal underlying pathophysiological mechanisms explaining the association between HDL and clinical outcome. METHODS AND RESULTS: We selected a cohort of 90 HF patients with 1:1 cardiovascular death/survivor ratio from BIOSTAT-CHF. A novel optimised protocol for selective enrichment of lipoproteins was used to prepare plasma. Enriched lipoprotein content of samples was analysed using high resolution nanoscale liquid chromatography-mass spectrometry-based proteomics, utilising a label free approach. Within the HDL proteome, 49 proteins significantly differed between deaths and survivors. An optimised model of 12 proteins predicted death with 76% accuracy (Nagelkerke R2 =0.37, P < 0.001). The strongest contributors to this model were filamin-A (related to crosslinking of actin filaments) [odds ratio (OR) 0.31, 95% confidence interval (CI) 0.15-0.61, P = 0.001] and pulmonary surfactant-associated protein B (related to alveolar capillary membrane function) (OR 2.50, 95% CI 1.57-3.98, P < 0.001). The model predicted mortality with an area under the curve of 0.82 (95% CI 0.77-0.87, P < 0.001). Internal cross validation resulted in 73.3 ± 7.2% accuracy. CONCLUSION: This study shows marked differences in composition of the HDL proteome between HF survivors and deaths. The strongest differences were seen in proteins reflecting crosslinking of actin filaments and alveolar capillary membrane function, posing potential pathophysiological mechanisms underlying the association between HDL and clinical outcome in HF.

Circulating microRNAs and hypertension--from new insights into blood pressure regulation to biomarkers of cardiovascular risk.

Hypertension is a leading cause of cardiovascular morbidity and mortality worldwide, yet the molecular mechanisms underpinning the development of high blood pressure remain incompletely understood. MicroRNAs are small, non-coding RNA molecules approximately 22 nucleotides in length that act as post-transcriptional regulators of gene expression. We highlight, through a review of recent literature, that studies on circulating microRNAs have provided novel insights into blood pressure regulation. They have also complemented tissue-based and animal-based experiments in shedding new light on our understanding of established pathways in hypertension, such as the renin-angiotensin system. Despite a number of challenges, we believe microRNAs herald particular potential in becoming effective biomarkers of target-organ damage in hypertension.

MicroRNAs in cardiovascular disease: an introduction for clinicians.

MicroRNAs (miRNAs) are small, non-coding, RNA molecules approximately 22 nucleotides in length which act as post-transcriptional regulators of gene expression. Individual miRNAs have been shown to regulate the expression of multiple genes. Conversely, the expression of individual genes can be regulated by multiple miRNAs. Consequently, since their discovery just over 20 years ago, miRNAs have been identified as key regulators of complex biological processes linked to multiple cardiovascular pathologies, including left ventricular hypertrophy, ischaemic heart disease, heart failure, hypertension and arrhythmias. Furthermore, since the finding that miRNAs are present in the circulation, they have been investigated as novel biomarkers, especially in the context of acute myocardial infarction (AMI) and heart failure. While there is little convincing evidence that miRNAs can outperform traditional biomarkers, such as cardiac troponins, in the diagnosis of AMI, there is potential for miRNAs to complement existing risk prediction models and act as valuable markers of post-AMI prognosis. Encouragingly, the concept of miRNA-based therapeutics is developing, with synthetic antagonists of miRNAs (antagomiRs) currently in phase II trials for the treatment of chronic hepatitis C virus infection. In the cardiovascular field, promising preclinical studies suggest that they could be useful in treating disorders ranging from heart failure to dyslipidaemia, although several challenges related to specificity and targeted delivery remain to be overcome. Through this review, we provide clinicians with a brief overview of the ever-expanding world of miRNAs.

A sequence variant associated with sortilin-1 (SORT1) on 1p13.3 is independently associated with abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a common human disease with a high estimated heritability (0.7); however, only a small number of associated genetic loci have been reported to date. In contrast, over 100 loci have now been reproducibly associated with either blood lipid profile and/or coronary artery disease (CAD) (both risk factors for AAA) in large-scale meta-analyses. This study employed a staged design to investigate whether the loci for these two phenotypes are also associated with AAA. Validated CAD and dyslipidaemia loci underwent screening using the Otago AAA genome-wide association data set. Putative associations underwent staged secondary validation in 10 additional cohorts. A novel association between the SORT1 (1p13.3) locus and AAA was identified. The rs599839 G allele, which has been previously associated with both dyslipidaemia and CAD, reached genome-wide significance in 11 combined independent cohorts (meta-analysis with 7048 AAA cases and 75 976 controls: G allele OR 0.81, 95% CI 0.76-0.85, P = 7.2 × 10(-14)). Modelling for confounding interactions of concurrent dyslipidaemia, heart disease and other risk factors suggested that this marker is an independent predictor of AAA susceptibility. In conclusion, a genetic marker associated with cardiovascular risk factors, and in particular concurrent vascular disease, appeared to independently contribute to susceptibility for AAA. Given the potential genetic overlap between risk factor and disease phenotypes, the use of well-characterized case-control cohorts allowing for modelling of cardiovascular disease risk confounders will be an important component in the future discovery of genetic markers for conditions such as AAA.

Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci.

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids.

Multi-ethnic analysis of lipid-associated loci: the NHLBI CARe project.

BACKGROUND: Whereas it is well established that plasma lipid levels have substantial heritability within populations, it remains unclear how many of the genetic determinants reported in previous studies (largely performed in European American cohorts) are relevant in different ethnicities. METHODOLOGY/PRINCIPAL FINDINGS: We tested a set of ∼50,000 polymorphisms from ∼2,000 candidate genes and genetic loci from genome-wide association studies (GWAS) for association with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) in 25,000 European Americans and 9,000 African Americans in the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe). We replicated associations for a number of genes in one or both ethnicities and identified a novel lipid-associated variant in a locus harboring ICAM1. We compared the architecture of genetic loci associated with lipids in both African Americans and European Americans and found that the same genes were relevant across ethnic groups but the specific associated variants at each gene often differed. CONCLUSIONS/SIGNIFICANCE: We identify or provide further evidence for a number of genetic determinants of plasma lipid levels through population association studies. In many loci the determinants appear to differ substantially between African Americans and European Americans.

Meta-analysis of Dense Genecentric Association Studies Reveals Common and Uncommon Variants Associated with Height.

Height is a classic complex trait with common variants in a growing list of genes known to contribute to the phenotype. Using a genecentric genotyping array targeted toward cardiovascular-related loci, comprising 49,320 SNPs across approximately 2000 loci, we evaluated the association of common and uncommon SNPs with adult height in 114,223 individuals from 47 studies and six ethnicities. A total of 64 loci contained a SNP associated with height at array-wide significance (p < 2.4 × 10(-6)), with 42 loci surpassing the conventional genome-wide significance threshold (p < 5 × 10(-8)). Common variants with minor allele frequencies greater than 5% were observed to be associated with height in 37 previously reported loci. In individuals of European ancestry, uncommon SNPs in IL11 and SMAD3, which would not be genotyped with the use of standard genome-wide genotyping arrays, were strongly associated with height (p < 3 × 10(-11)). Conditional analysis within associated regions revealed five additional variants associated with height independent of lead SNPs within the locus, suggesting allelic heterogeneity. Although underpowered to replicate findings from individuals of European ancestry, the direction of effect of associated variants was largely consistent in African American, South Asian, and Hispanic populations. Overall, we show that dense coverage of genes for uncommon SNPs, coupled with large-scale meta-analysis, can successfully identify additional variants associated with a common complex trait.

Change in serum lipids after acute coronary syndromes: secondary analysis of SPACE ROCKET study data and a comparative literature review.

BACKGROUND: It has long been an accepted belief that serum cholesterol significantly falls after myocardial infarction and that a return to pre-event levels takes approximately 3 months. The magnitude and clinical significance of this fall has recently been challenged. METHODS: In the Secondary Prevention of Acute Coronary Events-Reduction Of Cholesterol to Key European Targets (SPACE ROCKET) trial, we measured serum lipids of individuals on day 1 and between days 2 and 4 after acute myocardial infarction (AMI). Second, we performed a thorough literature review and compared all studies reporting data on absolute changes in lipids immediately after AMI, using weighted means. RESULTS: Of 1263 SPACE ROCKET participants, 128 had paired lipid measurements where both samples had been measured using identical methods at baseline and on days 2-4 after AMI. The mean lowering in total cholesterol between day 1 and day 2-4 was 0.71 mmol/L (95% CI 0.58-0.84; P < 0.0001) and in triglycerides was 0.10 mmol/L (-0.14-0.33; P = 0.405). A total of 25 papers showing absolute lipid changes post-AMI were identified. The combined data demonstrated a mean fall in total cholesterol of 9% to 11% from baseline over days 3-14 post-AMI, whereas for triglycerides, there was a rise of 18% from baseline to between day 9 and 12 weeks. CONCLUSIONS: After a secondary analysis of SPACE ROCKET data and a comparison of previously published data, we report a 10% fall in total cholesterol after AMI-a difference that is of high clinical significance. Consequently, measurement of serum lipids in patients with AMI should be performed within the first hours after presentation.

Hepatic metabolism and transporter gene variants enhance response to rosuvastatin in patients with acute myocardial infarction: the GEOSTAT-1 Study.

BACKGROUND: Pharmacogenetics aims to maximize benefits and minimize risks of drug treatment. Our objectives were to examine the influence of common variants of hepatic metabolism and transporter genes on the lipid-lowering response to statin therapy. METHODS AND RESULTS: The Genetic Effects On STATins (GEOSTAT-1) Study was a genetic substudy of Secondary Prevention of Acute Coronary Events-Reduction of Cholesterol to Key European Targets (SPACE ROCKET) (a randomized, controlled trial comparing 40 mg of simvastatin and 10 mg of rosuvastatin) that recruited 601 patients after myocardial infarction. We genotyped the following functional single nucleotide polymorphisms in the genes coding for the cytochrome P450 (CYP) metabolic enzymes, CYP2C9*2 (430C>T), CYP2C9*3 (1075A>C), CYP2C19*2 (681G>A), CYP3A5*1 (6986A>G), and hepatic influx and efflux transporters SLCO1B1 (521T>C) and breast cancer resistance protein (BCRP; 421C>A). We assessed 3-month LDL cholesterol levels and the proportion of patients reaching the current LDL cholesterol target of <70 mg/dL (<1.81 mmol/L). An enhanced response to rosuvastatin was seen for patients with variant genotypes of either CYP3A5 (P=0.006) or BCRP (P=0.010). Furthermore, multivariate logistic-regression analysis revealed that patients with at least 1 variant CYP3A5 and/or BCRP allele (n=186) were more likely to achieve the LDL cholesterol target (odds ratio: 2.289; 95% CI: 1.157, 4.527; P=0.017; rosuvastatin 54.0% to target vs simvastatin 33.7%). There were no differences for patients with variants of CYP2C9, CYP2C19, or SLCO1B1 in comparison with their respective wild types, nor were differential effects on statin response seen for patients with the most common genotypes for CYP3A5 and BCRP (n=415; odds ratio: 1.207; 95% CI: 0.768, 1.899; P=0.415). CONCLUSION: The LDL cholesterol target was achieved more frequently for the 1 in 3 patients with CYP3A5 and/or BCRP variant genotypes when prescribed rosuvastatin 10 mg, compared with simvastatin 40 mg. Clinical Trial Registration- URL: http://isrctn.org. Unique identifier: ISRCTN 89508434.

A randomized, controlled trial of simvastatin versus rosuvastatin in patients with acute myocardial infarction: the Secondary Prevention of Acute Coronary Events--Reduction of Cholesterol to Key European Targets Trial.

AIMS: We sought to evaluate reports that rosuvastatin 10 mg is a more efficacious treatment of hyperlipidaemia than is simvastatin 40 mg, hoping to assess this issue in the previously unstudied context of acute myocardial infarction. METHODS AND RESULTS: The Secondary Prevention of Acute Coronary Events - Reduction of Cholesterol to Key European Targets (SPACE ROCKET) Trial was an investigator-led, open-label, blinded-endpoint, multicentre, randomized, controlled trial assessing the proportion of patients, at 3 months, achieving European Society of Cardiology 2003 (ESC-03) lipid targets of total cholesterol (TC) less than 4.5 mmol/l (174 mg/dl) or low-density lipoprotein cholesterol (LDLc) less than 2.5 mmol/l (97 mg/dl). Of 1263 patients randomized, 77.6% simvastatin versus 79.9% rosuvastatin achieved ESC-03 targets [odds ratio (OR): 1.16; 95% confidence interval (CI): 0.88-1.53; P = 0.29]. There were statistically significant differences for simvastatin versus rosuvastatin, respectively, for mean LDLc 2.03 mmol/l (78 mg/dl) versus 1.94 mmol/l (75 mg/dl; P = 0.009) and also mean TC 3.88 mmol/l (150 mg/dl) versus 3.75 mmol/l (145 mg/dl; P = 0.005). A post-hoc analysis showed higher achievement of the new ESC, American Heart Association and American College of Cardiology optimal lipid target of LDLc less than 1.81 mmol/l (70 mg/dl) with rosuvastatin (45.0%) compared with simvastatin (37.8%; OR: 1.37; 95% CI: 1.09-1.72; P = 0.007). The proportion of patients achieving the Fourth Joint Task Force European Guidelines (2007) of TC less than 4.0 mmol/l (155 mg/dl) and LDLc less than 2.0 mmol/l (77 mg/dl) was 38.7% for simvastatin 40 mg and 47.7% for rosuvastatin 10 mg (OR: 1.48; 95% CI: 1.18-1.86; P = 0.001). CONCLUSION: We observed no superiority of either treatment for the ESC-03 lipid targets. Rosuvastatin 10 mg lowered mean cholesterol more effectively than simvastatin and achieved better results for the latest, more stringent, ESC target.