Haptoglobin is a hemoglobin-binding protein expressed by a genetic polymorphism as three major phenotypes: 1-1, 2-1, and 2-2. Most attention has been paid to determining haptoglobin phenotype as a genetic fingerprint used in forensic medicine. More recently, several functional differences between haptoglobin phenotypes have been demonstrated that appear to have important biological and clinical consequences. Haptoglobin polymorphism is associated with the prevalence and clinical evolution of many inflammatory diseases, including infections, atherosclerosis, and autoimmune disorders. These effects are explained by a phenotype-dependent modulation of oxidative stress and prostaglandin synthesis. Recent evidence is growing that haptoglobin is involved in the immune response as well. The strong genetic pressure favoring the 2-2 phenotype suggests an important role of haptoglobin in human pathology.

AimsTo 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 resultsExtensive 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.ConclusionWe 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.

Previous research has shown that nutrients and certain food items influence inflammation. However, little is known about the associations between diet, as a whole, and inflammatory markers. In the present study, we examined the ability of a FFQ-derived dietary inflammatory index (DII) to predict inflammation. Data from a Belgian cross-sectional study of 2524 generally healthy subjects (age 35–55 years) were used. The DII is a population-based, literature-derived dietary index that was developed to predict inflammation and inflammation-related chronic diseases. The DII was calculated from FFQ-derived dietary information and tested against inflammatory markers, namely C-reactive protein (CRP), IL-6, homocysteine and fibrinogen. Analyses were performed using multivariable logistic regression, adjusting for energy, age, sex, BMI, smoking status, education level, use of non-steroidal anti-inflammatory drugs, blood pressure, use of oral contraceptives, anti-hypertensive therapy, lipid-lowering drugs and physical activity. Multivariable analyses showed significant positive associations between the DII and the inflammatory markers IL-6 (>1·6 pg/ml) (OR 1·19, 95 % CI 1·04, 1·36) and homocysteine (>15 μmol/l) (OR 1·56, 95 % CI 1·25, 1·94). No significant associations were observed between the DII and the inflammatory markers CRP and fibrinogen. These results reinforce the fact that diet, as a whole, plays an important role in modifying inflammation.

SummaryEvidence assembled over the last decade shows that average telomere length (TL) acts as a biomarker for biological aging and cardiovascular disease (CVD) in particular. Although essential for a more profound understanding of the underlying mechanisms, little reference information is available on TL. We therefore sought to provide baseline TL information and assess the association of prevalent CVD risk factors with TL in subjects free of overt CVD within a small age range. We measured mean telomere restriction fragment length of peripheral blood leukocytes in a large, representative Asklepios study cohort of 2509 community-dwelling, Caucasian female and male volunteers aged approximately 35-55 years and free of overt CVD. We found a manifest age-dependent telomere attrition, at a significantly faster rate in men as compared to women. No significant associations were established with classical CVD risk factors such as cholesterol status and blood pressure, yet shorter TL was associated with increased levels of several inflammation and oxidative stress markers. Importantly, shorter telomere length was associated with an increasingly unhealthy lifestyle, particularly in men. All findings were age and gender adjusted where appropriate. With these cross-sectional results we show that TL of peripheral blood leukocytes primarily reflects the burden of increased oxidative stress and inflammation, whether or not determined by an increasingly unhealthy lifestyle, while the association with classical CVD risk factors is limited. This further clarifies the added value of TL as a biomarker for biological aging and might improve our understanding of how TL is associated with CVD.

BACKGROUND The European Atherosclerosis Society–European Federation of Clinical Chemistry and Laboratory Medicine Consensus Panel aims to provide recommendations to optimize atherogenic lipoprotein quantification for cardiovascular risk management. CONTENT We critically examined LDL cholesterol, non-HDL cholesterol, apolipoprotein B (apoB), and LDL particle number assays based on key criteria for medical application of biomarkers. (a) Analytical performance: Discordant LDL cholesterol quantification occurs when LDL cholesterol is measured or calculated with different assays, especially in patients with hypertriglyceridemia >175 mg/dL (2 mmol/L) and low LDL cholesterol concentrations <70 mg/dL (1.8 mmol/L). Increased lipoprotein(a) should be excluded in patients not achieving LDL cholesterol goals with treatment. Non-HDL cholesterol includes the atherogenic risk component of remnant cholesterol and can be calculated in a standard nonfasting lipid panel without additional expense. ApoB more accurately reflects LDL particle number. (b) Clinical performance: LDL cholesterol, non-HDL cholesterol, and apoB are comparable predictors of cardiovascular events in prospective population studies and clinical trials; however, discordance analysis of the markers improves risk prediction by adding remnant cholesterol (included in non-HDL cholesterol) and LDL particle number (with apoB) risk components to LDL cholesterol testing. (c) Clinical and cost-effectiveness: There is no consistent evidence yet that non-HDL cholesterol-, apoB-, or LDL particle-targeted treatment reduces the number of cardiovascular events and healthcare-related costs than treatment targeted to LDL cholesterol. SUMMARY Follow-up of pre- and on-treatment (measured or calculated) LDL cholesterol concentration in a patient should ideally be performed with the same documented test method. Non-HDL cholesterol (or apoB) should be the secondary treatment target in patients with mild to moderate hypertriglyceridemia, in whom LDL cholesterol measurement or calculation is less accurate and often less predictive of cardiovascular risk. Laboratories should report non-HDL cholesterol in all standard lipid panels.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, reaching 31% of deaths in 2012 [1]. In particular, atherosclerosis and ischemic heart disease (IHD) are the main causes of premature death in Europe and are responsible for 42% of deaths in women and 38% in men under 75 years old [2]. The global economic impact of CVD is estimated to have been US $906 billion in 2015 and is expected to rise by 22% by 2030 [3]. Cardiovascular diseases also represent the major cause of disability in developed countries. It has been estimated that their growing burden could lead to a global increase in loss of disability-adjusted life years (DALYs), from a loss of 85 million DALYs in 1990 to a loss of ~150 million DALYs in 2020, becoming a major non-psychological cause of lost productivity [4]. Several risk factors contribute to the etiology and development of CVD; they are divided into those modifiable through lifestyle changes or by taking a pharmacologic treatment (e.g. for hypertension, smoking, diabetes mellitus, hypercholesterolemia) and those that are not modifiable (age, male gender, and family history) [5]. Elevated total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) blood concentrations are the major modifiable risk factors for coronary heart disease (CHD), whereas high concentrations of plasma high-density lipoprotein cholesterol (HDL-C) in certain conditions are considered protective [6]. Moreover, LDL-C remains a fundamental CV risk factor (and a main target of therapy) even when statins are largely used in the general population [7]. An examination of the data of 18 053 participants aged ≥ 20 years who participated in the National Health and Nutrition Examination Surveys (NHANES) from 1999 to 2006 showed that the unadjusted prevalence of hypercholesterolemia ranged from 53.2% to 56.1% in United States adults [8]. Differences related to gender and race or ethnicity were observed; in particular, a lower rate of control was found among women than men and lower rates of having a cholesterol check and being told about hypercholesterolemia were reported by African Americans and Mexican Americans than whites [8]. A recent report from the American Heart Association confirmed that in the US only 75.7% of children and 46.6% of adults present targeted TC levels (TC < 170 mg/dl for children and < 200 mg/dl for adults, in untreated individuals) [9]. The pattern is similar in other Western countries [10, 11]

Background Previous research has shown that diet is associated with low-grade systemic inflammation among adults. However, no study has yet been conducted to explore the association between inflammatory potential of diet and low-grade systemic inflammation among adolescents whose dietary behavior may be different from adults. Methods We examine the predictive ability of 24-hour recall-derived dietary inflammatory index (DII) scores on inflammation among 532 European adolescents in the HELENA cross-sectional study. The DII is a literature-derived dietary index developed to predict inflammation. The DII was calculated per 1000 calories and was tested against C-reactive protein (CRP), interleukins (IL)-1,2,4, 10, TNF-α, ICAM, VCAM and IFN-γ. All inflammatory markers had non-normal distributions and therefore were log transformed. Analyses were performed using multivariable linear regression, adjusting for age, sex, city, BMI, smoking and physical activity. Results Pro-inflammatory diet (higher DII scores) was associated with increased levels of various inflammatory markers: TNF-α, IL-1, 2, IFN-γ and VCAM (bDIIt3vs1= 0.13, 95% CI: 0.001, 0.25; 0.13, 95% CI 0.001, 0.25; 0.40, 95% CI: 0.03, 0.77; 0.53, 95% CI: 0.05, 1.01; 0.07, 95% CI: 0.01, 0.13 respectively). Conclusion These results reinforce the fact that diet, as a whole, plays an important role in modifying inflammation in adolescents.

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