Hochschulschrift
Evaluation of food-based dietary guidelines of Germany concerning their potential of chronic disease prevention with suggestions for improvement: Results from the EPIC-Potsdam study
- Alternative title
-
Bewertung von deutschen lebensmittelbasierten Ernährungsempfehlungen hinsichtlich ihres Potenzials zur Prävention chronischer Erkrankungen mit Vorschlägen zur Verbesserung: Ergebnisse der EPIC-Potsdam-Studie
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
-
Berlin, Technische Universtität Berlin, Diss., 2012
- Keyword
-
Ernährung
Ernährungsberatung
Diätetik
- Event
-
Veröffentlichung
- (where)
-
Berlin
- (who)
-
Universitätsbibliothek der Technischen Universität Berlin
- (when)
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2012
- Creator
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Rüsten, Anne von
- Contributor
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Boeing, Heiner
- URN
-
urn:nbn:de:kobv:83-opus-36274
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:58 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Hochschulschrift
Associated
- Rüsten, Anne von
- Boeing, Heiner
- Universitätsbibliothek der Technischen Universität Berlin
Time of origin
- 2012
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Application of Gaussian graphical models to derive dietary intake networks and association of the identified networks with risk of major chronic diseases in European Prospective Investigation into Cancer and Nutrition-Potsdam Cohort
Physical activity and chronic disease risk : development and evaluation of a physical activity index and baseline physical activity data calibration in EPIC Germany
Habitual meal patterns in an EPIC-Potsdam sub-study : description, associations with cardiometabolic disease risk factors and multilevel modeling of energy and macronutrient intake
Treelet transform for untargeted metabolomics data : treelet transform generates serum metabolite and lipid components that are correlated to anthropometry and intestinal microbiota in a cross-sectional EPIC-Potsdam sub-study
The mediterranean diet and its components and the risk of heart failure in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study : a top down approach
Dentition status and the risk of chronic systemic diseases : the role of systemic inflammation, autoimmunity and oral microbiota
Macronutrient, vitamin, and mineral intakes in the EPIC-Germany cohorts
Investigation of the machine learning method Random Survival Forest as an exploratory analysis tool for the identification of variables associated with disease risks in complex survival data
Use of hormone replacement therapy (HRT) among women aged 45–64 years in the German EPIC-cohorts
Vitamin E intake and cardiovascular diseases in the EPIC-Potsdam Study
Quantity and quality of dietary fat, carbohydrate, and fiber intake in the German EPIC cohorts
Untersuchung des Zusammenhangs der alimentären Fettsäurezufuhr und des Fettsäurestatus in Erythrozyten mit dem Risiko für Typ-2-Diabetes mellitus in der EPIC-Potsdam-Studie
Application of Gaussian graphical models to derive dietary intake networks and association of the identified networks with risk of major chronic diseases in European Prospective Investigation into Cancer and Nutrition-Potsdam Cohort
Physical activity and chronic disease risk : development and evaluation of a physical activity index and baseline physical activity data calibration in EPIC Germany
Habitual meal patterns in an EPIC-Potsdam sub-study : description, associations with cardiometabolic disease risk factors and multilevel modeling of energy and macronutrient intake
Treelet transform for untargeted metabolomics data : treelet transform generates serum metabolite and lipid components that are correlated to anthropometry and intestinal microbiota in a cross-sectional EPIC-Potsdam sub-study
The mediterranean diet and its components and the risk of heart failure in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study : a top down approach
Dentition status and the risk of chronic systemic diseases : the role of systemic inflammation, autoimmunity and oral microbiota
Macronutrient, vitamin, and mineral intakes in the EPIC-Germany cohorts
Investigation of the machine learning method Random Survival Forest as an exploratory analysis tool for the identification of variables associated with disease risks in complex survival data
Use of hormone replacement therapy (HRT) among women aged 45–64 years in the German EPIC-cohorts
Vitamin E intake and cardiovascular diseases in the EPIC-Potsdam Study
Quantity and quality of dietary fat, carbohydrate, and fiber intake in the German EPIC cohorts
Untersuchung des Zusammenhangs der alimentären Fettsäurezufuhr und des Fettsäurestatus in Erythrozyten mit dem Risiko für Typ-2-Diabetes mellitus in der EPIC-Potsdam-Studie
Application of Gaussian graphical models to derive dietary intake networks and association of the identified networks with risk of major chronic diseases in European Prospective Investigation into Cancer and Nutrition-Potsdam Cohort
Physical activity and chronic disease risk : development and evaluation of a physical activity index and baseline physical activity data calibration in EPIC Germany
Habitual meal patterns in an EPIC-Potsdam sub-study : description, associations with cardiometabolic disease risk factors and multilevel modeling of energy and macronutrient intake
Treelet transform for untargeted metabolomics data : treelet transform generates serum metabolite and lipid components that are correlated to anthropometry and intestinal microbiota in a cross-sectional EPIC-Potsdam sub-study
The mediterranean diet and its components and the risk of heart failure in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study : a top down approach
Dentition status and the risk of chronic systemic diseases : the role of systemic inflammation, autoimmunity and oral microbiota
Macronutrient, vitamin, and mineral intakes in the EPIC-Germany cohorts
Investigation of the machine learning method Random Survival Forest as an exploratory analysis tool for the identification of variables associated with disease risks in complex survival data