Dietary energy density in relation to subsequent changes of weight and waist circumference in European men and women.

BACKGROUND: Experimental studies show that a reduction in dietary energy density (ED) is associated with reduced energy intake and body weight. However, few observational studies have investigated the role of ED on long-term weight and waist circumference change. METHODS AND PRINCIPAL FINDINGS: This population-based prospective cohort study included 89,432 participants from five European countries with mean age 53 years (range: 20-78 years) at baseline and were followed for an average of 6.5 years (range: 1.9-12.5 years). Participants were free of cancer, cardiovascular diseases and diabetes at baseline. ED was calculated as the energy intake (kcal) from foods divided by the weight (g) of foods. Multiple linear regression analyses were performed to investigate the associations of ED with annual weight and waist circumference change. Mean ED was 1.7 kcal/g and differed across study centers. After adjusting for baseline anthropometrics, demographic and lifestyle factors, follow-up duration and energy from beverages, ED was not associated with weight change, but significantly associated with waist circumference change overall. For 1 kcal/g ED, the annual weight change was -42 g/year [95% confidence interval (CI): -112, 28] and annual waist circumference change was 0.09 cm/year [95% CI: 0.01, 0.18]. In participants with baseline BMI<25 kg/m(2), 1 kcal/g ED was associated with a waist circumference change of 0.17 cm/year [95% CI: 0.09, 0.25]. CONCLUSION: Our results suggest that lower ED diets do not prevent weight gain but have a weak yet potentially beneficial effect on the prevention of abdominal obesity as measured by waist circumference

Reproductive factors and risk of hormone receptor positive and negative breast cancer: a cohort study

Background: The association of reproductive factors with hormone receptor (HR)-negative breast tumors remains uncertain. Methods: Within the EPIC cohort, Cox proportional hazards models were used to describe the relationships of reproductive factors (menarcheal age, time between menarche and first pregnancy, parity, number of children, age at first and last pregnancies, time since last full-term childbirth, breastfeeding, age at menopause, ever having an abortion and use of oral contraceptives [OC]) with risk of ER-PR-(n = 998) and ER+PR+ (n = 3,567) breast tumors. Results: A later first full-term childbirth was associated with increased risk of ER+PR+ tumors but not with risk of ER-PR-tumors (= 35 vs. = 19 years HR: 1.47 [95% CI 1.15-1.88] p(trend) < 0.001 for ER+PR+ tumors; = 35 vs. = 19 years HR: 0.93 [95% CI 0.53-1.65] p(trend) = 0.96 for ER-PR-tumors; P-het = 0.03). The risk associations of menarcheal age, and time period between menarche and first full-term childbirth with ER-PR-tumors were in the similar direction with risk of ER+PR+ tumors (p(het) = 0.50), although weaker in magnitude and statistically only borderline significant. Other parity related factors such as ever a full-term birth, number of births, age-and time since last birth were associated only with ER+PR+ malignancies, however no statistical heterogeneity between breast cancer subtypes was observed. Breastfeeding and OC use were generally not associated with breast cancer subtype risk. Conclusion: Our study provides possible evidence that age at menarche, and time between menarche and first full-term childbirth may be associated with the etiology of both HR-negative and HR-positive malignancies, although the associations with HR-negative breast cancer were only borderline significant

Genome-Wide Association Study and Functional Characterization Identifies Candidate Genes for Insulin-Stimulated Glucose Uptake

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in \u3e55,000 participants from three ancestry groups. We identified ten new loci (P \u3c 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits

Genetic insights into resting heart rate and its role in cardiovascular disease

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development

Characteristics of the study population across quintiles of dietary energy density (n = 89,432).

*<p>Expressed as means (or mean ± SD), otherwise indicated. Differences between quintile groups were tested using chi-square test (categorical variables) or ANOVA test (continuous variable). <i>P</i><0.0001 for all.</p>†<p>Energy-adjusted residuals of dietary variables.</p>#<p>1,273 participants with missing values.</p>‡<p>1,440 participants with missing values.</p>§<p>1,579 participants with missing values.</p>∥<p>3,319 participants with missing values.</p>¶<p>for women only.</p><p>Percentages are based on those participants with available data on that variable and may not sum to 100% due to rounding.</p

Association of energy density with annual weight change by baseline BMI^*.

<p>A: for participants with BMI<25 kg/m² (n = 41,914). B: for participants with baseline BMI≥25 kg/m² (n = 47,518). 95% CI: 95% confidence interval of regression coefficients. Regression coefficients represent the annual weight change (g/year) for 1 kcal/g ED. The overall estimate was based on random-effect model. ^* Adjusted for follow-up time and baseline age, height and weight, smoking, physical activity, education, alcohol intake, menopausal status, hormone replace therapy use, and energy intake from beverages.</p

Association of energy density with annual waist circumference change by baseline BMI^*.

<p>Association of energy density with annual waist circumference change by baseline BMI^*.</p

Association of energy density with annual waist circumference change (n = 89,432)^*.

<p>95% CI: 95% confidence interval of regression coefficients. Regression coefficients represent the annual waist circumference change (cm/year) for 1 kcal/g ED. The overall estimate was based on random-effect model. ^* Adjusted for follow-up time and baseline age, height, weight, and waist circumference, smoking, physical activity, education, alcohol intake, menopausal status, hormone replace therapy use, and energy intake from beverages.</p

Relationships of food groups and macronutrients with dietary energy density (kcal/g) (n = 89,432).

*<p>β regression coefficients refer to the energy density (kcal/g) difference explained by 100 g foods.</p>†<p>Only food or nutrient items had Partial R²>0.01 were listed here.</p>‡<p>β regression coefficients refer to the energy density (kcal/g) difference explained by 1% of energy contributed by individual nutrient.</p