Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study

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

Peroxisome Proliferator-Activated Receptor Activation is Associated with Altered Plasma One-Carbon Metabolites and B-Vitamin Status in Rats

Plasma concentrations of metabolites along the choline oxidation pathway have been linked to increased risk of major lifestyle diseases, and peroxisome proliferator-activated receptors (PPARs) have been suggested to be involved in the regulation of key enzymes along this pathway. In this study, we investigated the effect of PPAR activation on circulating and urinary one-carbon metabolites as well as markers of B-vitamin status. Male Wistar rats (n = 20) received for 50 weeks either a high-fat control diet or a high-fat diet with tetradecylthioacetic acid (TTA), a modified fatty acid and pan-PPAR agonist with high affinity towards PPARα. Hepatic gene expression of PPARα, PPARβ/δ and the enzymes involved in the choline oxidation pathway were analyzed and concentrations of metabolites were analyzed in plasma and urine. TTA treatment altered most biomarkers, and the largest effect sizes were observed for plasma concentrations of dimethylglycine, nicotinamide, methylnicotinamide, methylmalonic acid and pyridoxal, which were all higher in the TTA group (all p < 0.01). Hepatic Pparα mRNA was increased after TTA treatment, but genes of the choline oxidation pathway were not affected. Long-term TTA treatment was associated with pronounced alterations on the plasma and urinary concentrations of metabolites related to one-carbon metabolism and B-vitamin status in rats

Assessment of urinary betaine as a marker of diabetes mellitus in cardiovascular patients.

Abnormal urinary excretion of betaine has been demonstrated in patients with diabetes or metabolic syndrome. We aimed to identify the main predictors of excretion in cardiovascular patients and to make initial assessment of its feasibility as a risk marker of future diabetes development. We used data from 2396 patients participating in the Western Norway B-vitamin Intervention Trial, who delivered urine and blood samples at baseline, and in the majority at two visits during follow-up of median 39 months. Betaine in urine and plasma were measured by liquid-chromatography-tandem mass spectrometry. The strongest determinants of urinary betaine excretion by multiple regression were diabetes mellitus, age and estimated glomerular filtration rate; all p<0.001. Patients with diabetes mellitus (n = 264) had a median excretion more than three times higher than those without. We found a distinct non-linear association between urinary betaine excretion and glycated hemoglobin, with a break-point at 6.5%, and glycated hemoglobin was the strongest determinant of betaine excretion in patients with diabetes mellitus. The discriminatory power for diabetes mellitus corresponded to an area under the curve by receiver-operating characteristics of 0.82, and betaine excretion had a coefficient of reliability of 0.73. We also found a significant, independent log-linear relation between baseline betaine excretion and the risk of developing new diabetes during follow-up. The good discriminatory power for diabetes, high test-retest stability and independent association with future risk of new diabetes should motivate further investigation on the role of betaine excretion in risk assessment and long-term follow-up of diabetes mellitus

Density plot showing the relative distributions of urinary betaine excretion in patients with diabetes (black line), no diabetes and serum glucose ≥5.5 mmol/L (green line), and no diabetes and serum glucose <5.5 mmol/L (blue line).

<p>Density plot showing the relative distributions of urinary betaine excretion in patients with diabetes (black line), no diabetes and serum glucose ≥5.5 mmol/L (green line), and no diabetes and serum glucose <5.5 mmol/L (blue line).</p

Generalized linear models showing age and gender adjusted associations between urinary betaine excretion and estimated glomerular filtration rate (A) and plasma betaine (B).

<p>Results, with 95% confidence intervals, for patients with diabetes mellitus (black line), no diabetes and serum glucose ≥5.5 mmol/L (green line), and no diabetes and serum glucose <5.5 mmol/L (blue line) are shown. Lines at the bottom indicate distribution.</p

Generalized additive model showing the non-linear association between urinary betaine excretion and glycated hemoglobin (HbA1c).

<p>The dotted lines mark the 95% confidence interval. The line at the bottom indicates the distribution.</p

Determinants of urinary betaine excretion at baseline by linear regression.

<p>Abbreviations: eGFR estimated glomerular filtration rate; UACR urinary albumin/creatinine ratio.</p>a<p>Adjusted for all variables in the model.</p>b<p>Standardized beta-coefficient.</p>c<p>Log transformed.</p><p>Not significant in age and gender adjusted analysis: Acute coronary syndrome, diuretics, ACE-inhibitors, fasting blood sample, N-vessel disease, total cholesterol, low density lipoprotein, apo lipoprotein A1, apo lipoprotein B, CRP.</p

Cox regression survival models.

<p>Abbreviations: CVD cardiovascular disease.</p><p>*Covariates: age, sex, study site, participation in the WENBIT study, smoking, family history of coronary heart disease, medical history (hypertension, diabetes, acute myocardial infarction, percutaneous intervention, coronary artery bypass surgery, peripheral vascular disease, cerebrovascular disease, cured cancer, active cancer (not in the cardiovascular mortality analyses due to failure of the model to converge), deep vein thrombosis or vein surgery, pulmonary disease, atrial fibrillation, dyspnea grade 0–4), measured parameters at baseline (body mass index, ECG rhythm, left ventricular ejection fraction, number of coronary vessels with >50% stenosis), laboratory values (estimated glomerular filtration rate, uric acid, hemoglobin, potassium, sodium, C-reactive protein, glycated hemoglobin, low density lipoprotein, troponin T), medication (aspirin, adenosine diphosphate receptor inhibitor, warfarin, angiotensin converting enzyme inhibitor and/or angiotensin receptor blocker, beta-blocker, digoxin, spironolactone, thiazide, calcium antagonist, nitrate, statin, insulin, metformin, sulfonylurea, treatment for chronic obstructive pulmonary disease, non-steroid anti-inflammatory drug, corticosteroid, antidepressant, antipsychotic), and baseline revascularization.</p><p>Cox regression survival models.</p

Baseline characteristics after matching.

<p>Continuous variables are shown as means (standard deviation) and medians (interquartile range) and categorical variables as numbers (percentage). Abbreviations: WENBIT = WEstern Norway B-vitamin Trial; CAD = coronary artery disease; PCI = percutaneous coronary intervention; CABG = coronary artery bypass graft; PVD = peripheral vascular disease; DVT = deep venous thrombosis; NYHA = New York Heart Association; BP = blood pressure; eGFR = estimated glomerular filtration rate; HbA1c = glycated hemoglobin; WBC = white blood cell count; ACEI = angiotensin converting enzyme inhibitor; ARB = angiotensin receptor blocker; COPD = chronic obstructive pulmonary disease; NSAID = non-steroid anti-inflammatory drug.</p><p>Baseline characteristics after matching.</p

Determinants of urinary betaine excretion in diabetes by linear regression.

<p>Abbreviations: eGFR estimated glomerular filtration rate.</p>a<p>Adjusted for all variables in the model.</p>b<p>Standardized beta-coefficient.</p>c<p>Antidiabetic medication vs no antidiabetic medication.</p><p>Not significant in age and gender adjusted analysis: body mass index, smoking, hypertension, type of diabetes, total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apo lipoprotein A1, triglycerides, urinary albumin/creatinine ratio.</p