Nachhaltigkeit in der Gemeinschaftsverpflegung : das Forschungsprojekt NAHGAST

<p>Means are adjusted for age, sex, height, weight and energy and weighted for day and season of recall (N = 34,436). The reference circle of the radius (100%) correspond to the ‘EPIC means’ and the spikes indicate the deviation of the specific nutrient mean in quintiles of pattern scores from the reference ‘EPIC means’.</p

Glycemic index, glycemic load, and risk of coronary heart disease: a pan-European cohort study.

BACKGROUND:High carbohydrate intake raises blood triglycerides, glucose, and insulin; reduces HDLs; and may increase risk of coronary heart disease (CHD). Epidemiological studies indicate that high dietary glycemic index (GI) and glycemic load (GL) are associated with increased CHD risk. OBJECTIVES:The aim of this study was to determine whether dietary GI, GL, and available carbohydrates are associated with CHD risk in both sexes. METHODS:This large prospective study-the European Prospective Investigation into Cancer and Nutrition-consisted of 338,325 participants who completed a dietary questionnaire. HRs with 95% CIs for a CHD event, in relation to intake of GI, GL, and carbohydrates, were estimated using covariate-adjusted Cox proportional hazard models. RESULTS:After 12.8 y (median), 6378 participants had experienced a CHD event. High GL was associated with greater CHD risk [HR 1.16 (95% CI: 1.02, 1.31) highest vs. lowest quintile, p-trend 0.035; HR 1.18 (95% CI: 1.07, 1.29) per 50 g/day of GL intake]. The association between GL and CHD risk was evident in subjects with BMI (in kg/m2) ≥25 [HR: 1.22 (95% CI: 1.11, 1.35) per 50 g/d] but not in those with BMI <25 [HR: 1.09 (95% CI: 0.98, 1.22) per 50 g/d) (P-interaction = 0.022). The GL-CHD association did not differ between men [HR: 1.19 (95% CI: 1.08, 1.30) per 50 g/d] and women [HR: 1.22 (95% CI: 1.07, 1.40) per 50 g/d] (test for interaction not significant). GI was associated with CHD risk only in the continuous model [HR: 1.04 (95% CI: 1.00, 1.08) per 5 units/d]. High available carbohydrate was associated with greater CHD risk [HR: 1.11 (95% CI: 1.03, 1.18) per 50 g/d]. High sugar intake was associated with greater CHD risk [HR: 1.09 (95% CI: 1.02, 1.17) per 50 g/d]. CONCLUSIONS:This large pan-European study provides robust additional support for the hypothesis that a diet that induces a high glucose response is associated with greater CHD risk

The associations between plasma phospholipid fatty acids and percent of weight change at 5 years were investigated using a multinomial logistic regression model.

<p>The percent of weight change at 5 years was estimated as (weightat 5 years minus weight at baseline/weight at baseline)*100, and expressed as a percentage. Weight change during the follow-up was categorized according to tertiles considering the middle category as the reference category (i.e. stable weight at 5 years in %, -1.59;2.83%). The highest tertile (3, weight gain in %, >2.83%) and the lowest (1, weight loss in %, <-1.59%) were compared to the reference category. Exposure variables (fatty acid concentrations 2 log-transformed) were modeled as continuous variables. The model was adjusted by length of follow-up, age, energy and alcohol intakes, smoking status, physical activity, and region. Analyses were carried out for women and men separately.</p

The EPIC regions (n = 16) were ordered from South to North.

<p>These geographical regions were designated by grouping some of the 23 EPIC centers together: France (Paris and surroundings), Northern Italy (Varese), Central Italy (Florence), Southern Italy (Naples), Northern Spain (San Sebastian, Navarra, Asturias), South-Eastern Spain (Murcia), Southern Spain (Granada), Greece (Athens and other regions), Northern Sweden (Umeå), Southern Sweden (Malmö), Denmark (Aarhus and Copenhagen), UK (Oxford, the health conscious group, vegans and ovo-lacto-vegetarians), UK (Cambridge, the General population), The Netherlands (Utrecht and Bilthoven), former East Germany (Potsdam), and South-West Germany (Heidelberg).</p

Deviation (%) of the 24-HDR mean intakes from the overall EPIC means among participants in the quintiles of PC1 scores for nutrients (A) and foods (B).

<p>Means are adjusted for age, sex, height, weight and energy and weighted for day and season of recall (N = 34,436). The reference circle of the radius (100%) correspond to the ‘EPIC means’ and the spikes indicate the deviation of the specific nutrient mean in quintiles of pattern scores from the reference ‘EPIC means’.</p

Proportion of the variance in each EPIC center captured in an overall PCA on combined data by the number of PC retained.

<p>Proportion of the variance in each EPIC center captured in an overall PCA on combined data by the number of PC retained.</p

Risk of colorectal cancer incidence associated with metabolic health (hyperinsulinaemia)–defined body size phenotypes using body mass index or the International Diabetes Federation waist circumference cut-points.

<p>Risk of colorectal cancer incidence associated with metabolic health (hyperinsulinaemia)–defined body size phenotypes using body mass index or the International Diabetes Federation waist circumference cut-points.</p

P-values of F-test on type III sum of squares estimate.

<p>*The scores had means of 0 but are standardized to unit variance; PC scores calculated on the country-specific FFQ derived intake levels of 23 nutrients, n = 477,312.</p>†<p>10 units increase.</p>‡<p>10 years increase.</p>#<p>Degree of Freedom.</p

Baseline characteristics of control group participants by metabolic health (hyperinsulinaemia)–defined body size phenotypes using body mass index or the International Diabetes Federation waist circumference cut-points.

<p>Baseline characteristics of control group participants by metabolic health (hyperinsulinaemia)–defined body size phenotypes using body mass index or the International Diabetes Federation waist circumference cut-points.</p

Adjusted 5y weight change (in g/5y) for the iso-energetic increase of 5% of energy from one macronutrient (↑) at the expense of 5% of energy from another macronutrient (↓) according to gender before and after calibration (n = 373,803).¹

1<p>2-levels (individuals within centers) linear mixed models adjusted for age, energy from non alcohol source, energy from alcohol, initial BMI, smoking status, education, physical activity, follow-up-time and plausible total energy intake reporting according to Goldberg (fixed effects). Intercept and macronutrient slope were entered as random effects. ² Calibrated dietary data were obtained from country- and sex-specific calibration models. The 24-hour dietary values were regressed on the dietary values obtained from the main dietary questionnaire, adjusting for age, BMI at baseline total energy from non alcohol sources, energy from alcohol sources and study center. The sampling distribution of days and seasons of 24-hour dietary recall administration was corrected using a set of weights to reproduce an even distribution of recalls across weekday and season. The standard error of the coefficient was estimated using bootstrap sampling (10 loops). ³ Further adjusted for the percentage of protein. ⁴ Further adjusted for the percentage of fat. ⁵ Further adjusted for the percentage of carbohydrates. ⁶ Further adjusted for the percentage of plant fat. ⁷ Further adjusted for the percentage of unknown fat. ⁸ Further adjusted for the percentage of animal fat. ⁹ Further adjusted for the percentage of plant protein. ¹⁰ Further adjusted for the percentage of unknown protein. ¹¹ Further adjusted for the percentage of animal protein. ¹² Further adjusted for the percentage of starch. ¹³ Further adjusted for the percentage of sugar.</p