Summary Background Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear. Methods We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts. Findings The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14–1·83) and the presence of either LPA SNP (1·88, 1·40–2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81–1·11 and either LPA SNP 1·10, 0·92–1·31) or cardiovascular mortality (0·99, 0·81–1·2 and 1·13, 0·90–1·40, respectively) or in the validation studies. Interpretation In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established. Funding Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny.

Background: Reliability of real-time PCR (RT-qPCR) data is dependent on the use of appropriate reference gene(s) for normalization. To date, no validated reference genes have been reported for normalizing gene expression in human myocardium. This study aimed to identify validated reference genes for use in gene expression studies of failed and non-failed human myocardium.

Background: Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery. Methods: We employed aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at one month post-MI in a New Zealand cohort (CDCS) including 181 post-MI patients who were subsequently hospitalized for HF compared with 250 post-MI patients who remained event-free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially co-regulated in post-MI HF using Weighted Gene Co-expression Network Analysis (WCGNA). A Singapore cohort (IMMACULATE) of 223 post-MI patients, of which 33 patients were hospitalized for HF (median follow-up 2.0 years), was used for further candidate enrichment of plasma proteins using Fisher meta-analysis, resampling-based statistical testing and machine learning. We then cross-referenced differentially-expressed proteins with their differentially-expressed genes from single-cell transcriptomes of non-myocyte cardiac cells isolated from a murine MI model, and single-cell and single-nuclei transcriptomes of cardiac myocytes from murine HF models and human HF patients. Results: In the CDCS cohort, 212 differentially-expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. WCGNA prioritised 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF compared with event-free controls (dataset 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (dataset 2) while single-cell transcriptomes identified 15 gene-protein candidates (dataset 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly-enriched proteins that were common to all 3 datasets included well-established biomarkers of post-MI HF - N-terminal B-type natriuretic peptide and troponin T - as well as newly-emergent biomarkers - angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4 and follistatin-related protein-3. Conclusions: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac cell transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

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