Genovefa Kolovou

Assessment and Clinical Relevance of Non-Fasting and Postprandial Triglycerides: An Expert Panel Statement

An Expert Panel group of scientists and clinicians met to consider several aspects related to non-fasting and postprandial triglycerides (TGs) and their role as risk factors for cardiovascular disease (CVD). In this context, we review recent epidemiological studies relevant to elevated non-fasting TGs as a risk factor for CVD and provide a suggested classification of non-fasting TG concentration. Secondly, we sought to describe methodologies to evaluate postprandial TG using a fat tolerance test (FTT) in the clinic. Thirdly, we discuss the role of non-fasting lipids in the treatment of postprandial hyperlipemia. Finally, we provide a series of clinical recommendations relating to non-fasting TGs based on the consensus of the Expert Panel: 1). Elevated non-fasting TGs are a risk factor for CVD. 2). The desirable non-fasting TG concentration is <2 mmol/l (<180 mg/dl). 3). For standardized postprandial testing, a single FTT meal should be given after an 8 h fast and should consist of 75 g of fat, 25 g of carbohydrates and 10 g of protein. 4). A single TG measurement 4 h after a FTT meal provides a good evaluation of the postprandial TG response. 5). Preferably, subjects with non-fasting TG levels of 1-2 mmol/l (89-180 mg/dl) should be tested with a FTT. 6). TG concentration ≤ 2.5 mmol/l (220 mg/dl) at any time after a FTT meal should be considered as a desirable postprandial TG response. 7). A higher and undesirable postprandial TG response could be treated by aggressive lifestyle modification (including nutritional supplementation) and/or TG lowering drugs like statins, fibrates and nicotinic acid.

A randomized trial of genotype-guided dosing of acenocoumarol and phenprocoumon.

BACKGROUND Observational evidence suggests that the use of a genotype-guided dosing algorithm may increase the effectiveness and safety of acenocoumarol and phenprocoumon therapy. METHODS We conducted two single-blind, randomized trials comparing a genotype-guided dosing algorithm that included clinical variables and genotyping for CYP2C9 and VKORC1 with a dosing algorithm that included only clinical variables, for the initiation of acenocoumarol or phenprocoumon treatment in patients with atrial fibrillation or venous thromboembolism. The primary outcome was the percentage of time in the target range for the international normalized ratio (INR; target range, 2.0 to 3.0) in the 12-week period after the initiation of therapy. Owing to low enrollment, the two trials were combined for analysis. The primary outcome was assessed in patients who remained in the trial for at least 10 weeks. RESULTS A total of 548 patients were enrolled (273 patients in the genotype-guided group and 275 in the control group). The follow-up was at least 10 weeks for 239 patients in the genotype-guided group and 245 in the control group. The percentage of time in the therapeutic INR range was 61.6% for patients receiving genotype-guided dosing and 60.2% for those receiving clinically guided dosing (P=0.52). There were no significant differences between the two groups for several secondary outcomes. The percentage of time in the therapeutic range during the first 4 weeks after the initiation of treatment in the two groups was 52.8% and 47.5% (P=0.02), respectively. There were no significant differences with respect to the incidence of bleeding or thromboembolic events. CONCLUSIONS Genotype-guided dosing of acenocoumarol or phenprocoumon did not improve the percentage of time in the therapeutic INR range during the 12 weeks after the initiation of therapy. (Funded by the European Commission Seventh Framework Programme and others; EU-PACT ClinicalTrials.gov numbers, NCT01119261 and NCT01119274.).

Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration cut-points—a joint consensus statement from the European Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine

Abstract Aims To critically evaluate the clinical implications of the use of non-fasting rather than fasting lipid profiles and to provide guidance for the laboratory reporting of abnormal non-fasting or fasting lipid profiles. Methods and results Extensive observational data, in which random non-fasting lipid profiles have been compared with those determined under fasting conditions, indicate that the maximal mean changes at 1–6 h after habitual meals are not clinically significant [+0.3 mmol/L (26 mg/dL) for triglycerides; −0.2 mmol/L (8 mg/dL) for total cholesterol; −0.2 mmol/L (8 mg/dL) for LDL cholesterol; +0.2 mmol/L (8 mg/dL) for calculated remnant cholesterol; −0.2 mmol/L (8 mg/dL) for calculated non-HDL cholesterol]; concentrations of HDL cholesterol, apolipoprotein A1, apolipoprotein B, and lipoprotein(a) are not affected by fasting/non-fasting status. In addition, non-fasting and fasting concentrations vary similarly over time and are comparable in the prediction of cardiovascular disease. To improve patient compliance with lipid testing, we therefore recommend the routine use of non-fasting lipid profiles, while fasting sampling may be considered when non-fasting triglycerides >5 mmol/L (440 mg/dL). For non-fasting samples, laboratory reports should flag abnormal concentrations as triglycerides ≥2 mmol/L (175 mg/dL), total cholesterol ≥5 mmol/L (190 mg/dL), LDL cholesterol ≥3 mmol/L (115 mg/dL), calculated remnant cholesterol ≥0.9 mmol/L (35 mg/dL), calculated non-HDL cholesterol ≥3.9 mmol/L (150 mg/dL), HDL cholesterol ≤1 mmol/L (40 mg/dL), apolipoprotein A1 ≤1.25 g/L (125 mg/dL), apolipoprotein B ≥1.0 g/L (100 mg/dL), and lipoprotein(a) ≥50 mg/dL (80th percentile); for fasting samples, abnormal concentrations correspond to triglycerides ≥1.7 mmol/L (150 mg/dL). Life-threatening concentrations require separate referral when triglycerides >10 mmol/L (880 mg/dL) for the risk of pancreatitis, LDL cholesterol >13 mmol/L (500 mg/dL) for homozygous familial hypercholesterolaemia, LDL cholesterol >5 mmol/L (190 mg/dL) for heterozygous familial hypercholesterolaemia, and lipoprotein(a) >150 mg/dL (99th percentile) for very high cardiovascular risk. Conclusion We recommend that non-fasting blood samples be routinely used for the assessment of plasma lipid profiles. Laboratory reports should flag abnormal values on the basis of desirable concentration cut-points. Non-fasting and fasting measurements should be complementary but not mutually exclusive.

The prevalence of metabolic syndrome in various populations.

The insulin resistance/metabolic syndrome is characterized by the variable co-existence of hyperinsulinemia, obesity, dyslipidemia (small dense low-density lipoprotein, hypertriglyceridemia, and decreased high-density lipoprotein cholesterol), and hypertension. The pathogenesis of the syndrome has multiple origins. However, obesity and sedentary lifestyle coupled with diet and still largely unknown genetic factors clearly interact to produce the syndrome. This multifactorial and complex trait of metabolic syndrome leads to increased risk of cardiovascular disease. The scope of this review is to examine the differences in prevalence of the metabolic syndrome in various groups (eg, according to age, sex, ethnicity, social status, or presence of obesity) that could help with the better understanding of the pathogenesis of this syndrome. This review also considers the impact of metabolic syndrome on cardiovascular disease.

Postprandial Lipemia in Men with Metabolic Syndrome, Hypertensives and Healthy Subjects

BackgroundThe metabolic syndrome (MetS), as well as postprandial hypertriglyceridemia, is associated with coronary heart disease. This study aimed to evaluate the postprandial lipemia after oral fat tolerance test (OFTT) in subjects with MetS and compare them to hypertensive (HTN) and healthy subjects.ResultsOFTT was given to 33 men with MetS (defined by the Adult Treatment Panel III), 17 HTN and 14 healthy men. The MetS group was further divided according to fasting triglycerides (TG) into TG ≥ 150 [MetS+TG, (n = 22)] or <150 mg/dl [MetS-TG (n = 11)], and into those with or without hypertension [MetS+HTN (n = 24), MetS-HTN (n = 9), respectively]. TG concentrations were measured before and at 4, 6 and 8 h after OFTT and the postprandial response was quantified using the area under the curve (AUC) for TG.The postprandial response was significantly higher in MetS compared to HTN and healthy men [AUC (SD) in mg/dl/h; 2534 ± 1016 vs. 1620 ± 494 and 1019 ± 280, respectively, p ≤ 0.001]. The TG levels were increased significantly in MetS+TG compared to MetS-TG subjects at 4 (p = 0.022), 6 (p < 0.001) and 8 hours (p < 0.001). The TG were increased significantly in MetS-TG compared to healthy subjects at 4 (p = 0.011), 6 (p = 0.001) and 8 hours (p = 0.015). In linear regression analysis only fasting TG levels were a significant predictor of the AUC (Coefficient B = 8.462, p < 0.001).ConclusionFasting TG concentration is the main determinant of postprandial lipemia. However, an exaggeration of TG postprandialy was found in normotriglyceridemic MetS and HTN compared to healthy subjects. This suggests that intervention to lower fasting TG levels should be recommended in MetS subjects.

Myocarditis as a precipitating factor for heart failure: evaluation and 1‐year follow‐up using cardiovascular magnetic resonance and endomyocardial biopsy

The aim of this study was to evaluate myocarditis as a precipitating factor for heart failure using cardiovascular magnetic resonance (CMR) and endomyocardial biopsy

Meta-Analysis Investigating Associations Between Healthy Diet and Fasting Glucose and Insulin Levels and Modification by Loci Associated With Glucose Homeostasis in Data From 15 Cohorts

Whether loci that influence fasting glucose (FG) and fasting insulin (FI) levels, as identified by genome-wide association studies, modify associations of diet with FG or FI is unknown. We utilized data from 15 U.S. and European cohort studies comprising 51,289 persons without diabetes to test whether genotype and diet interact to influence FG or FI concentration. We constructed a diet score using study-specific quartile rankings for intakes of whole grains, fish, fruits, vegetables, and nuts/seeds (favorable) and red/processed meats, sweets, sugared beverages, and fried potatoes (unfavorable). We used linear regression within studies, followed by inverse-variance-weighted meta-analysis, to quantify 1) associations of diet score with FG and FI levels and 2) interactions of diet score with 16 FG-associated loci and 2 FI-associated loci. Diet score (per unit increase) was inversely associated with FG (β = -0.004 mmol/L, 95% confidence interval: -0.005, -0.003) and FI (β = -0.008 ln-pmol/L, 95% confidence interval: -0.009, -0.007) levels after adjustment for demographic factors, lifestyle, and body mass index. Genotype variation at the studied loci did not modify these associations. Healthier diets were associated with lower FG and FI concentrations regardless of genotype at previously replicated FG- and FI-associated loci. Studies focusing on genomic regions that do not yield highly statistically significant associations from main-effect genome-wide association studies may be more fruitful in identifying diet-gene interactions.

Apolipoprotein E knockout models.

Atherosclerosis is a multifactorial and long-lasting process in humans. Therefore, animal models where more rapid changes occur can be useful for the study of this process. Among such models are the apolipoprotein (apo) E knock out mice. Apo E deficient mice show impaired clearing of plasma lipoproteins and they develop atherosclerosis in a short time. The current review considers lipid metabolism and inflammation as well as nutritional and pharmacological agents affecting atherosclerosis, using the apo E knock out mouse model.

Association of apolipoprotein E polymorphism with myocardial infarction in Greek patients with coronary artery disease.

Summary Studies in several populations have indicated that genetic variation at the apolipoprotein E (apoE) structural locus influences the risk of coronary artery disease (CAD) and myocardial infarction (MI). This study aimed at investigating whether apoE polymorphism has an allelic and/or genotypic impact on the risk of MI in Greek patients with CAD. We compared apoE gene polymorphism in a group of patients with angiographically confirmed CAD but not MI [CAD/MI (–)-group, n = 143] and a group of age and sex-matched CAD patients who had experienced a non-fatal MI [CAD/MI (+)-group, n = 124]. The patients were also compared with a group of healthy younger individuals (n = 240) with no family history of CAD. The apoE genotype distribution differed significantly between the two groups of CAD patients (p = 0.02). The ϵ2 allele was 5.3-fold less frequent in the CAD/MI (+)-group compared with the CAD/MI (-)-group (1.2% vs. 6.3%, p = 0.01). The frequency of the ϵ2 allele in healthy subjects was 8.1%, which is 6.8-fold higher than in CAD/MI (+)-patients (p = 0.001) and twice as high compared with all CAD patients (p = 0.02). No differences in ϵ4 allele frequencies were observed between CAD/MI (+)- and CAD/MI (–)-patients (10.9% vs. 9.8%), or between patients with CAD and healthy subjects (10.3% vs. 10.2%). In summary, the ϵ4 allele was not found to be associated with an increased risk for CAD or MI. In contrast, a negative association of the ϵ2 allele with MI was observed among Greek patients with CAD.

Myocardial Inflammation in Autoimmune Diseases: Investigation by Cardiovascular Magnetic Resonance and Endomyocardial Biopsy

INTRODUCTION Myocardial inflammation often coexists with different types of autoimmune diseases. Our aim was to investigate the presence of myocarditis in these patients by Cardiovascular Magnetic Resonance (CMR) and endomyocardial biopsy. PATIENTS-METHODS Twenty patients, aged 20-55 yrs with autoimmune diseases and cardiac symptoms (3 with Takayasus arteritis, 3 with systemic lupus erythematosus, 5 with rheumatoid arthritis, 7 with autoimmune thyroid disease and 2 with systemic sclerosis) and 20 patients with the same autoimmune diseases but without cardiac symptoms (controls) were studied. The presence of myocarditis and LV function were evaluated by CMR. Myocarditis was documented using T2-weighted (T2-W), T1-weighted (T1-W) before and after contrast media injection and late enhanced images. In 10 patients (positive for myocarditis by CMR with either low LVEF or recent increase in troponin), endomyocardial biopsy was also performed. Myocardial specimens were evaluated by histology and polymerase chain reaction techniques (PCR). RESULTS Myocarditis was identified in 18/20 patients by CMR. In the T2-W images the signal ratio of myocardium to skeletal muscle was 1.89+/-0.25 (control values 1.57+/-0.13, p<0.05). From the T1-W images the relative myocardial enhancement was 11.31+/-11.18 (control values 3.09+/-0.05, p<0.05). Epicardial late gadolinium enhanced areas were identified in 18/20. In myocardial specimens, histology revealed inflammation in 5/10 (50%) and PCR documented viral or microbial genomes in 8/10 (80%). Positive histology and PCR were in agreement with 50% and 80% of positive CMR examinations, respectively. Herpes virus was identified in 3/10, Adeno in 1/10, Coxsackie B6 in 1/10, echo in 1/10, Parvo-B19 in 3/10, CMV in 1/10 and Chlamydia trachomatis in 8/10. CONCLUSIONS Myocardial inflammation is a common finding in patients with autoimmune diseases and cardiac symptoms. The diagnosis can be confirmed by CMR, which is a noninvasive and reliable tool for the investigation of these patients.