Lipoproteins and lipoprotein lipid composition are associated with stages of dysglycemia and subclinical coronary atherosclerosis.

Background: Dyslipidaemia in patients with diabetes contributes to the risk of atherosclerotic cardiovascular disease. We aimed to identify a dyslipidemic profile associated with both dysglycemia and subclinical coronary atherosclerosis.

Methods: Study participants (n = 5050) were classified in three groups: normoglycemia, pre-diabetes, and diabetes.

The impact of steatotic liver disease on coronary artery disease through changes in the plasma lipidome.

Steatotic liver disease has been shown to associate with cardiovascular disease independently of other risk factors. Lipoproteins have been shown to mediate some of this relationship but there remains unexplained variance. Here we investigate the plasma lipidomic changes associated with liver steatosis and the mediating effect of these lipids on coronary artery disease (CAD).

Quantifying Triglyceride-Rich Lipoprotein Atherogenicity, Associations With Inflammation, and Implications for Risk Assessment Using Non-HDL Cholesterol.

Background: Triglyceride-rich lipoproteins and remnants (TRL/remnants) have a causal, but not yet quantified, relationship with coronary heart disease (CHD): myocardial infarction plus revascularization.

Objectives: The authors sought to estimate TRL/remnant per-particle atherogenicity, investigate causal relationships with inflammation, and determine whether differences in the atherogenicity of TRL/remnants and low-density lipoprotein (LDL) impact the causal association of non-high-density lipoprotein cholesterol (non-HDL-C) with CHD.

Methods: Single nucleotide polymorphisms (SNPs) (N = 1,357) identified by genome-wide association in the UK Biobank were ranked into 10 clusters according to the effect on TRL/remnant-C vs LDL-C.

Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study.

Background: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis.