👤 Christian T Ruff

Apolipoprotein B concentration reflects the number of atherogenic lipoproteins and is recognized as a key lipid risk marker. Whether the type or size of apoB particle (apoB-P) adds predictive value for coronary artery disease (CAD) remains unclear. A prospective analysis of 207 368 UK Biobank participants with comprehensive lipoprotein profiling and no prior history of atherosclerotic disease, diabetes, or active lipid-lowering therapy was conducted. Multivariable-adjusted Cox regression models were used to examine the association between each of the following lipid parameters with incident CAD: (i) nuclear magnetic resonance-measured apoB-P, (ii) concentrations of individual lipoprotein classes [very-low-density lipoprotein (VLDL), low-density lipoprotein (LDL)], (iii) size subclasses, (iv) average particle diameter, and (v) immunoassay-measured lipoprotein(a) [Lp(a)]. A one standard deviation (SD) increase in apoB-P was associated with a 33% higher CAD risk [hazard ratio (HR): 1.33, 95% CI: 1.30-1.36]. Although VLDL particles were observed to carry a higher per-particle risk (HR per 100 nmol/L: 1.22, 1.11-1.34) compared with LDL (HR per 100 nmol/L: 1.07, 1.05-1.08), this difference was counterbalanced after considering relative particle abundance (LDL 91% vs VLDL 9% of total apoB-P). Thus the respective HR per 1-SD were 1.09 (1.05-1.14) and 1.24 (1.19-1.30). Particle diameter or size subclasses were not associated with CAD after apoB-P adjustment. The association of Lp(a) was robust even after apoB-P adjustment (HR:1.18, 1.16-1.20) and added independent prognostic value for CAD (area under curve: 0.769 vs 0.774, P < .001). Lipid-related atherosclerotic risk is most accurately reflected by the total count of apoB-P and is largely unaffected by the major particle type (VLDL, LDL) or size. Elevated count of Lp(a) adds additional risk, and thus adequate assessment of atherogenic risk from dyslipidemia is best accomplished by consideration of both apoB-P and Lp(a) concentrations. Show less

Atrial fibrillation (AF) is a prevalent and morbid abnormality of the heart rhythm with a strong genetic component. Here, we meta-analyzed genome and exome sequencing data from 36 studies that included 52,416 AF cases and 277,762 controls. In burden tests of rare coding variation, we identified novel associations between AF and the genes MYBPC3, LMNA, PKP2, FAM189A2 and KDM5B. We further identified associations between AF and rare structural variants owing to deletions in CTNNA3 and duplications of GATA4. We broadly replicated our findings in independent samples from MyCode, deCODE and UK Biobank. Finally, we found that CRISPR knockout of KDM5B in stem-cell-derived atrial cardiomyocytes led to a shortening of the action potential duration and widespread transcriptomic dysregulation of genes relevant to atrial homeostasis and conduction. Our results highlight the contribution of rare coding and structural variants to AF, including genetic links between AF and cardiomyopathies, and expand our understanding of the rare variant architecture for this common arrhythmia. Show less

Acyl-CoA synthetase 1 (ACSL1) is an enzyme that converts fatty acids to acyl-CoA-derivatives for lipid catabolism and lipid synthesis in general and can provide substrates for the production of mediators of inflammation in monocytes and macrophages. Acsl1 expression is increased by hyperglycemia and inflammatory stimuli in monocytes and macrophages, and promotes the pro-atherosclerotic effects of diabetes in mice. Yet, surprisingly little is known about the mechanisms underlying Acsl1 transcriptional regulation. Here we demonstrate that the glucose-sensing transcription factor, Carbohydrate Response Element Binding Protein (CHREBP), is a regulator of the expression of Acsl1 mRNA by high glucose in mouse bone marrow-derived macrophages (BMDMs). In addition, we show that inflammatory stimulation of BMDMs with lipopolysaccharide (LPS) increases Acsl1 mRNA via the transcription factor, NF-kappa B. LPS treatment also increases ACSL1 protein abundance and localization to membranes where it can exert its activity. Using an Acsl1 reporter gene containing the promoter and an upstream regulatory region, which has multiple conserved CHREBP and NF-kappa B (p65/RELA) binding sites, we found increased Acsl1 promoter activity upon CHREBP and p65/RELA expression. We also show that CHREBP and p65/RELA occupy the Acsl1 promoter in BMDMs. In primary human monocytes cultured in high glucose versus normal glucose, ACSL1 mRNA expression was elevated by high glucose and further enhanced by LPS treatment. Our findings demonstrate that CHREBP and NF-kappa B control Acsl1 expression under hyperglycemic and inflammatory conditions. Show less

The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 × 10 HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction. Show less