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Aims To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and results We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10−14 to 1.0 × 10−6 (discovery) and P = 5.6 × 10−10 to 1.1 × 10−2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P Conclusion Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.

The National Institute of Child Health and Human Development's Nulliparous Pregnancy Outcomes Study-Monitoring Mothers-to-Be (nuMoM2b) Heart Health Study (HHS) was designed to investigate the relationships between adverse pregnancy outcomes and modifiable risk factors for cardiovascular disease. The ongoing nuMoM2b-HHS, which started in 2013, is a prospective follow-up of the nuMoM2b cohort, which included 10,038 women recruited between 2010 and 2013 from 8 centers across the United States who were initially observed over the course of their first pregnancies. In this report, we detail the design and study procedures of the nuMoM2b-HHS. Women in the pregnancy cohort who consented to be contacted for participation in future studies were approached at 6-month intervals to ascertain health information and to maintain ongoing contact. Two to 5 years after completion of the pregnancy documented in the nuMoM2b, women in the nuMoM2b-HHS were invited to an in-person study visit. During this visit, they completed psychosocial and medical history questionnaires and had clinical measurements and biological specimens obtained. A subcohort of participants who had objective assessments of sleep-disordered breathing during pregnancy were asked to repeat this investigation. This unique prospective observational study includes a large, geographically and ethnically diverse cohort, rich depth of phenotypic information about adverse pregnancy outcomes, and clinical data and biospecimens from early in the index pregnancy onward. Data obtained from this cohort will provide mechanistic and clinical insights into how data on a first pregnancy can provide information about the potential development of subsequent risk factors for cardiovascular disease.

The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.

Background: The extent to which adult height, a biomarker of the interplay of genetic endowment and early-life experiences, is related to risk of chronic diseases in adulthood is uncertain. Methods: We calculated hazard ratios (HRs) for height, assessed in increments of 6.5 cm, using individual-participant data on 174 374 deaths or major non-fatal vascular outcomes recorded among 1 085 949 people in 121 prospective studies. Results: For people born between 1900 and 1960, mean adult height increased 0.5-1 cm with each successive decade of birth. After adjustment for age, sex, smoking and year of birth, HRs per 6.5 cm greater height were 0.97 (95% confidence interval: 0.96-0.99) for death from any cause, 0.94 (0.93-0.96) for death from vascular causes, 1.04 (1.03-1.06) for death from cancer and 0.92 (0.90-0.94) for death from other causes. Height was negatively associated with death from coronary disease, stroke subtypes, heart failure, stomach and oral cancers, chronic obstructive pulmonary disease, mental disorders, liver disease and external causes. In contrast, height was positively associated with death from ruptured aortic aneurysm, pulmonary embolism, melanoma and cancers of the pancreas, endocrine and nervous systems, ovary, breast, prostate, colorectum, blood and lung. HRs per 6.5 cm greater height ranged from 1.26 (1.12-1.42) for risk of melanoma death to 0.84 (0.80-0.89) for risk of death from chronic obstructive pulmonary disease. HRs were not appreciably altered after further adjustment for adiposity, blood pressure, lipids, inflammation biomarkers, diabetes mellitus, alcohol consumption or socio-economic indicators. Conclusion: Adult height has directionally opposing relationships with risk of death from several different major causes of chronic diseases. Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2012; all rights reserved.

In this issue of the Journal, an expert panel offers 7 recommendations on how population studies supported by National Heart, Lung, and Blood Institute contracts might be strategically transformed (Am J Epidemiol. 2015;181(6):363-368). The Institute and its external advisors seemingly established this panel of epidemiologists and nonepidemiologists primarily to find ways to save research costs. Although the working group's recommendations offer reasonable approaches, we believe that, even in tough fiscal times, the main drivers of cardiovascular epidemiologic research must remain 1) scientific questions that are important and 2) study designs to match these. Although cardiovascular epidemiology admittedly is often redundant and needs to be more efficient, undue focus on administrative efficiency and cost savings will not necessarily guarantee cutting-edge population research.

In 2013, the National Heart, Lung, and Blood Institute assembled a working group on epidemiology and population sciences from its Advisory Council and Board of External Experts. The working group was charged with making recommendations to the National Heart, Lung, and Blood Advisory Council about how the National Heart, Lung, and Blood Institute could take advantage of new scientific opportunities and delineate future directions for the epidemiology of heart, lung, blood, and sleep diseases. Seven actionable recommendations were proposed for consideration. The themes included 1) defining the compelling scientific questions and challenges in population sciences and epidemiology of heart, lung, blood, and sleep diseases; 2) developing methods and training mechanisms to integrate "big data" science into the practice of epidemiology; 3) creating a cohort consortium and inventory of major studies to optimize the efficient use of data and specimens; and 4) fostering a more open, competitive approach to evaluating large-scale longitudinal epidemiology and population studies. By building on the track record of success of the heart, lung, blood, and sleep cohorts to leverage new data science opportunities and encourage broad research and training partnerships, these recommendations lay a strong foundation for the transformation of heart, lung, blood, and sleep epidemiology.

A recent article in the American Journal of Epidemiology by Whelton et al. (1) prompted this brief commentary about the use of the word “elevated” in medical reports. The definition of the adjective elevated includes the following concepts: 1) raised up, especially above the ground or above the normal level (e.g., an elevated pulse); 2) exalted or noble; lofty (e.g., elevated thoughts); and 3) elated or joyful (2). In medicine and medical research, the term elevated is often used when the more accurate, and indeed more correct, word is “higher,” as in higher concentrations of cholesterol are associated with increased risk of coronary heart disease. Given that we understand that the association between cholesterol concentration and coronary heart disease risk is continuous, the word we should use to describe this relationship is higher rather than elevated (3), because any increase in concentration is associated with greater risk. As a corollary to the elimination of the inappropriate use of elevated as a description of changed clinical measurements and consistent with proper use in clinical chemistry, we suggest that the word “level” should also be eliminated in this context. Level describes an amount, such as the level of water in a jug, level of interest in problem, level of intelligence in an individual, or even level of blood pressure. Level is not the correct term for concentration (which is mass per unit volume) or rate (as in beats per minute), both of which should be described as higher or lower but never elevated. C-reactive protein is a particular case in point. Concentrations in the plasma or serum of healthy individuals and those with diseases range from approximately 50 µg/L to more than 500 mg/L (10,000-fold higher), but the median value in healthy US and European adults is 1–2 mg/L. In 90% of healthy subjects, the plasma C-reactive protein concentration is less than 3 mg/L, and this value has been frequently considered the cutpoint between normal and elevated. However, there is no actual threshold at the concentration of 3 mg/L, and the use of the term elevated creates confusion as to nature of the relationship of acute inflammation and infection and/or tissue damage with high values of C-reactive protein that still fall within the normal range versus that with truly increased concentrations. Kohli and Cannon (3) recently emphasized the importance of matching language to the type of research (e.g., higher values versus increased values or higher risk versus increased risk) so as not to mislead the reader of an observational study into thinking that the data derive from an interventional study. Similarly, authors need to use precise language to describe increased concentrations; for example, they must distinguish between values of C-reactive protein that fall within the upper limits of the reference range for healthy subjects and the much higher values in subjects who are mounting an acute phase response to a clinically significant pathological process. By striving for improved accuracy in reporting, we may avoid unnecessarily misleading the reader as to the meaning of the findings.

The authors assessed associations of body size perception and weight change over 13 years in black men and women and white men and women from the Coronary Artery Risk Development in Young Adults (CARDIA) Study (1992-2005). The perceptions of self and ideal body size were measured by using the Stunkard 9-figure scale at the year 7 examination (1992-1993). Figures were classified into underweight, normal weight, overweight, and obese. Self-ideal discrepancy yielded 4 body size satisfaction categories. Body mass index (BMI) (measured at years 7, 10, 15, and 20) was the dependent variable in gender-specific adjusted multiple regression models stratified by year 7 BMI. Obese women who perceived themselves as obese lost 0.09 BMI units annually, while those who perceived themselves as normal weight gained 0.31 units annually (P = 0.0005); obese women who considered their body size much too large had less annual weight gain than did those who considered their body size a bit too large (0.21 vs. 0.38 BMI units; P = 0.009). Obese women with overweight ideal body size gained less weight annually than did those with normal weight ideal body size (0.12 vs. 0.27 BMI units; P = 0.04). Results for men showed fewer and weaker associations. When obese women perceive themselves as obese and feel that their body size is too large, they gain less weight over time.