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OBJECTIVE-Fetuin-A, a hepatic secretory protein that simultaneously inhibits arterial calcification and insulin action, is associated with type 2 diabetes, but its association with cardiovascular disease (CVD) is uncertain. Preliminary studies suggest that

It has been suggested that a high-fat meal may acutely impair endothelium-dependent vasodilation and that this impairment may be prevented by concomitant intake of antioxidants. Because red wine contains antioxidant polyphenols and may reduce cardiovascular disease, we examined the effect of red wine on postprandial endothelial function. Using a crossover design, 13 healthy volunteers consumed a high-fat meal (0.8 g fat/kg body weight) with red wine (3 ml/kg) or an isocaloric control beverage on 2 separate days, 1 week apart. Flow-mediated dilation of the brachial artery was examined by vascular ultrasound at baseline and at 2, 4, and 6 hours after the meal. At these times, flow-mediated dilation with the high-fat meal and control beverage was 9.5 +/- 5.0%, 7.9 +/- 5.1%, 6.8 +/- 3.6%, and 7.3 +/- 4.6%, respectively (nonsignificant trend). There was also a nonsignificant trend for flow-mediated dilation after the high-fat meal with wine: 8.0 +/- 4.1%, 5.7 +/- 4.7%, 6.4 +/- 3.1%, and 6.9 +/- 3.8%, respectively. There was no difference in the effects between wine and the control beverage (p = 0.77). Triglycerides increased 2- to 2.7-fold over baseline (p = 0.0001) with a peak occurring 5 hours after the high-fat meals. In contrast to previous studies, the present study did not demonstrate a significant effect of a high-fat meal on endothelial vasomotor function in healthy subjects. Under these conditions, we did not demonstrate a beneficial acute effect of red wine on endothelial function. [ABSTRACT FROM AUTHOR]

Tasnim F Imran,1–3,* Daniel Posner,1,4,* Jacqueline Honerlaw,1 Jason L Vassy,1,2 Rebecca J Song,1 Yuk-Lam Ho,1 Steven J Kittner,5 Katherine P Liao,1,2 Tianxi Cai,1,6 Christopher J O’Donnell,1,2 Luc Djousse,1,2 David R Gagnon,1,4 J Michael Gaziano,1,2 Peter WF Wilson,7,8 Kelly Cho1,2 On behalf of the VA Million Veteran Program 1Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA; 2Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3Department of Medicine, Cardiology Section, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA; 4Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; 5Department of Neurology, Baltimore VA Medical Center and University of Maryland School of Medicine, Baltimore, MD, USA; 6Harvard T. H. Chan School of Public Health, Boston, MA, USA; 7Atlanta VA Medical Center, Decatur, GA, USA; 8Department of Medicine, Division of Cardiovascular Disease, Emory University School of Medicine, Atlanta, GA, USA *These authors contributed equally to this work Background: Large databases provide an efficient way to analyze patient data. A challenge with these databases is the inconsistency of ICD codes and a potential for inaccurate ascertainment of cases. The purpose of this study was to develop and validate a reliable protocol to identify cases of acute ischemic stroke (AIS) from a large national database.Methods: Using the national Veterans Affairs electronic health-record system, Center for Medicare and Medicaid Services, and National Death Index data, we developed an algorithm to identify cases of AIS. Using a combination of inpatient and outpatient ICD9 codes, we selected cases of AIS and controls from 1992 to 2014. Diagnoses determined after medical-chart review were considered the gold standard. We used a machine-learning algorithm and a neural network approach to identify AIS from ICD9 codes and electronic health-record information and compared it with a previous rule-based stroke-classification algorithm.Results: We reviewed administrative hospital data, ICD9 codes, and medical records of 268 patients in detail. Compared with the gold standard, this AIS algorithm had a sensitivity of 91%, specificity of 95%, and positive predictive value of 88%. A total of 80,508 highly likely cases of AIS were identified using the algorithm in the Veterans Affairs national cardiovascular disease-risk cohort (n=2,114,458).Conclusion: Our algorithm had high specificity for identifying AIS in a nationwide electronic health-record system. This approach may be utilized in other electronic health databases to accurately identify patients with AIS. Keywords: acute ischemic stroke, algorithm, large databases, big data, administrative health data, cerebrovascular accident

Objective: To examine the relation of fatty acid-binding protein (FABP)4 and nonesterified fatty acids (NEFAs) to diabetes in older adults.Research Design and Methods: We ascertained incident diabetes among 3,740 Cardiovascular Health Study participants (1992-2007) based on the use of hypoglycemic medications, fasting glucose ≥ 126 mg/dL, or nonfasting glucose ≥ 200 mg/dL. FABP4 and NEFA were measured on specimens collected between 1992 and 1993.Results: Mean age of the 3,740 subjects studied was 74.8 years. For each SD increase in log FABP4, hazard ratios (HRs) for diabetes were 1.35 (95% CI 1.10-1.65) for women and 1.45 (1.13-1.85) for men controlling for age, race, education, physical activity, cystatin C, alcohol intake, smoking, self-reported health status, and estrogen use for women (P for sex-FABP4 interaction 0.10). BMI modified the FABP4-diabetes relation (P = 0.009 overall; 0.02 for women and 0.135 for men), in that statistically significant higher risk of diabetes was mainly seen in men with BMI <25 kg/m(2) (HR per SD: 1.78 [95% CI 1.13-2.81]). There was a modest and nonsignificant association of NEFA with diabetes (P(trend) = 0.21). However, when restricted to the first 5 years of follow-up, multivariable-adjusted HRs for diabetes were 1.0 (ref.), 1.68 (95% CI 1.12-2.53), and 1.63 (1.07-2.50) across consecutive tertiles of NEFA (P(trend) = 0.03).Conclusions: Plasma FABP4 was positively associated with incident diabetes in older adults, and such association was statistically significant in lean men only. A significant positive association between plasma NEFA and incident diabetes was observed during the first 5 years of follow-up. [ABSTRACT FROM AUTHOR]

BACKGROUND: Heart failure is highly prevalent among older adults and is associated with high cost and societal burden. Although previous studies have reported beneficial effects of dietary factors on heart failure predictors, no previous study has examined whether frequent consumption of nuts is associated with a lower risk of heart failure in a large prospective cohort. OBJECTIVE: We examined the association between nut consumption and incident heart failure to determine whether such a relation is modified by overweight or obesity. DESIGN: This was a prospective cohort study of 20,976 participants from the Physicians' Health Study I. Nut consumption was assessed with a simple abbreviated food questionnaire, and self-reported heart failure was ascertained by follow-up questionnaires. We used Cox regression to estimate relative risks of heart failure. RESULTS: After an average follow-up of 19.6 y, 1,093 new cases of heart failure occurred. Nut consumption was not associated with the risk of developing heart failure in this cohort: multivariable adjusted hazard ratios were 1.0 (reference), 0.98 (95% CI: 0.83, 1.15), 1.06 (95% CI: 0.89, 1.27), and 1.01 (95% CI: 0.84, 1.22) for nut consumption of <1, 1, and > or =2 servings/wk, respectively (P for linear trend: 0.64). The lack of a meaningful relation between nut intake and incident heart failure was seen in both lean and overweight or obese participants (P for interaction: 0.96). CONCLUSION: Our data do not provide evidence for an association between nut consumption and incident heart failure in US male physicians. However, our data cannot rule out possible benefits of nut consumption on subtypes of heart failure not prevalent in this cohort. Copyright © 2008 American Society for Nutrition [ABSTRACT FROM AUTHOR]

Background: Reports on the association between alcohol consumption and the risk of lung cancer have been inconsistent. The purpose of this study was to assess this association in a cohort study.Methods: This study included 4265 participants in the original population-based Framingham Study cohort and 4973 subjects in the offspring cohort. Alcohol consumption data were collected periodically for both cohorts. We used the risk sets method to match control subjects to each case patient based on age, sex, smoking variables, and year of birth. We used a conditional logistic regression model to estimate the relative risk of lung cancer according to alcohol consumption.Results: Alcohol consumption was generally light to moderate (i.e., <12 g/day) in both cohorts. During mean follow-ups of 32.8 years in the original and 16.2 years in the offspring cohorts, 269 cases of lung cancer occurred. In categories of total alcohol consumption of 0, 0.1-12, 12.1-24, and greater than 24 g/day, the crude incidence rates of lung cancer were 7.4, 13.6, 16.4, and 25.2 cases per 10 000 person-years, respectively, in the original cohort and 6.6, 4.3, 7.9, and 12.3 cases per 10 000 person-years, respectively, in the offspring cohort. However, after adjustment for age, sex, pack-years of smoking, smoking status, and year of birth in a multivariable conditional logistic regression model, relative risks for lung cancer from the lowest to the highest category of alcohol consumption were 1.0 (referent), 1.0 (95% confidence interval [CI] = 0.5 to 2.1), 1.0 (95% CI = 0.5 to 2.3), and 1.1 (95% CI = 0.5 to 2.3), respectively, in the original cohort and 1.0, 1.4 (95% CI = 0.5 to 3.6), 1.1 (95% CI = 0.3 to 3.6), and 2.0 (95% CI = 0.7 to 5.7), respectively, in the offspring cohort.Conclusion: Alcohol consumption among subjects in the Framingham Study, most of whom were light to moderate drinkers, was not statistically significantly associated with the risk of lung cancer. [ABSTRACT FROM AUTHOR]

Abstract Background Atrial Fibrillation is highly prevalent in clinical practice affecting approximately 2.3 million people in USA and 4.5 million people in European Union. The aim of the study was to examine the association between nut consumption and incident atrial fibrillation. Methods Prospective cohort of 21,054 male participants of Physicians' Health Study I. Nut consumption was estimated using food frequency questionnaire and incident atrial fibrillation was ascertained through yearly follow-up questionnaires. Cox regression was used to estimate relative risks of atrial fibrillation. Results The average age was 54.6 ± 9.5 years (40.7-87.1). During a mean follow up of 20 years (median 24 years), 3,317 cases of atrial fibrillation occurred. The crude incidence rate was 7.6, 7.4, 8.2, 7.9, and 6.8 cases/1000 person-years for people reporting nut consumption of rarely/never, 1-3/month, 1/per week, 2-6/week, and ≥ 7/week, respectively. Multivariable adjusted hazard ratios (95% CI) for incident atrial fibrillation were 1.00 (ref), 1.00 (0.90-1.11), 1.09 (0.97-1.21), 1.07 (0.95-1.21), and 0.91 (0.70-1.17) for nut consumption from the lowest to the highest category of nut consumption (p for trend 0.26). No statistically significant association between nut consumption and atrial fibrillation was found when stratified by body mass index (BMI < 25 vs ≥ 25 kg/m2) or age (< 65 vs. ≥ 65 years). Conclusions Our data did not show an association between nut consumption and incident atrial fibrillation among US male physicians.

Abstract Background We sought to examine whether ε4 carrier status modifies the relation between body mass index (BMI) and HDL. The National Heart, Lung, and Blood Institute Family Heart Study included 657 families with high family risk scores for coronary heart disease and 588 randomly selected families of probands in the Framingham, Atherosclerosis Risk in Communities, and Utah Family Health Tree studies. We selected 1402 subjects who had ε4 carrier status available. We used generalized estimating equations to examine the interaction between BMI and ε4 allele carrier status on HDL after adjusting for age, gender, smoking, alcohol intake, mono- and poly-unsaturated fat intake, exercise, comorbidities, LDL, and family cluster. Results The mean (standard deviation) age of included subjects was 56.4(11.0) years and 47% were male. Adjusted means of HDL for normal, overweight, and obese BMI categories were 51.2(± 0.97), 45.0(± 0.75), and 41.6(± 0.93), respectively, among 397 ε4 carriers (p for trend < 0.0001) and 53.6(± 0.62), 51.3(± 0.49), and 45.0(± 0.62), respectively, among 1005 non-carriers of the ε4 allele (p-value for trend < 0.0001). There was no evidence for an interaction between BMI and ε4 status on HDL(p-value 0.39). Conclusion Our findings do not support an interaction between ε4 allele status and BMI on HDL.

Abstract Context The metabolic syndrome (MetS), in addition to its lipid, metabolic, and anthropomorphic characteristics, is associated with a prothrombotic and the proinflammatory state. However, the relationship of inflammatory biomarkers to MetS is not clear. Objective To study the association between a group of thrombotic and inflammatory biomarkers and the MetS. Methods Ten conventional MetS risk variables and ten biomarkers were analyzed. Correlations, factor analysis, hexagonal binning, and regression of each biomarker with the National Cholesterol Education Program (NCEP) MetS categories were performed in the Family Heart Study (n = 2,762). Results Subjects in the top 75% quartile for plasminogen activator inhibitor-1 (PAI1) had a 6.9 CI95 [4.2–11.2] greater odds (p < 0.0001) of being classified with the NCEP MetS. Significant associations of the corresponding top 75% quartile to MetS were identified for monocyte chemotactic protein 1 (MCP1, OR = 2.19), C-reactive protein (CRP, OR = 1.89), interleukin-6 (IL6, OR = 2.11), sICAM1 (OR = 1.61), and fibrinogen (OR = 1.86). PAI1 correlated significantly with all obesity and dyslipidemia variables. CRP had a high correlation with serum amyloid A (0.6) and IL6 (0.51), and a significant correlation with fibrinogen (0.46). Ten conventional quantitative risk factors were utilized to perform multivariate factor analysis. Individual inclusion, in this analysis of each biomarker, showed that, PAI1, CRP, IL6, and fibrinogen were the most important biomarkers that clustered with the MetS latent factors. Conclusion PAI1 is an important risk factor for MetS. It correlates significantly with most of the variables studied, clusters in two latent factors related to obesity and lipids, and demonstrates the greatest relative odds of the 10 biomarkers studied with respect to the MetS. Three other biomarkers, CRP, IL6, and fibrinogen associate also importantly with the MetS cluster. These 4 biomarkers can contribute in the MetS risk assessment.