Food based dietary patterns and chronic disease prevention

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Abstract

Matthias B Schulze and colleagues discuss current knowledge on the associations between dietary patterns and cancer, coronary heart disease, stroke, and type 2 diabetes, focusing on areas of uncertainty and future research directions

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Most cited references 28

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Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis.

An Lian Pan, Qi Sun, Adam M. Bernstein … (2011)

The relation between consumption of different types of red meats and risk of type 2 diabetes (T2D) remains uncertain. We evaluated the association between unprocessed and processed red meat consumption and incident T2D in US adults. We followed 37,083 men in the Health Professionals Follow-Up Study (1986-2006), 79,570 women in the Nurses' Health Study I (1980-2008), and 87,504 women in the Nurses' Health Study II (1991-2005). Diet was assessed by validated food-frequency questionnaires, and data were updated every 4 y. Incident T2D was confirmed by a validated supplementary questionnaire. During 4,033,322 person-years of follow-up, we documented 13,759 incident T2D cases. After adjustment for age, BMI, and other lifestyle and dietary risk factors, both unprocessed and processed red meat intakes were positively associated with T2D risk in each cohort (all P-trend <0.001). The pooled HRs (95% CIs) for a one serving/d increase in unprocessed, processed, and total red meat consumption were 1.12 (1.08, 1.16), 1.32 (1.25, 1.40), and 1.14 (1.10, 1.18), respectively. The results were confirmed by a meta-analysis (442,101 participants and 28,228 diabetes cases): the RRs (95% CIs) were 1.19 (1.04, 1.37) and 1.51 (1.25, 1.83) for 100 g unprocessed red meat/d and for 50 g processed red meat/d, respectively. We estimated that substitutions of one serving of nuts, low-fat dairy, and whole grains per day for one serving of red meat per day were associated with a 16-35% lower risk of T2D. Our results suggest that red meat consumption, particularly processed red meat, is associated with an increased risk of T2D.

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Application of a new statistical method to derive dietary patterns in nutritional epidemiology.

Kurt Hoffmann, Matthias B. Schulze, Anja Schienkiewitz … (2004)

Because foods are consumed in combination, it is difficult in observational studies to separate the effects of single foods on the development of diseases. A possible way to examine the combined effect of food intakes is to derive dietary patterns by using appropriate statistical methods. The objective of this study was to apply a new statistical method, reduced rank regression (RRR), that is more flexible and powerful than the classic principal component analysis. RRR can be used efficiently in nutritional epidemiology by choosing disease-specific response variables and determining combinations of food intake that explain as much response variation as possible. The authors applied RRR to extract dietary patterns from 49 food groups, specifying four diabetes-related nutrients and nutrient ratios as responses. Data were derived from a nested German case-control study within the European Prospective Investigation into Cancer and Nutrition-Potsdam study consisting of 193 cases with incident type 2 diabetes identified until 2001 and 385 controls. The four factors extracted by RRR explained 93.1% of response variation, whereas the first four factors obtained by principal component analysis accounted for only 41.9%. In contrast to principal component analysis and other methods, the new RRR method extracted a significant risk factor for diabetes.

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Comparison of weight loss among named diet programs in overweight and obese adults: a meta-analysis.

Bradley C. Johnston, Steve Kanters, Kristofer Bandayrel … (2014)

Many claims have been made regarding the superiority of one diet or another for inducing weight loss. Which diet is best remains unclear.

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Author and article information

Contributors

Matthias B Schulze: Role: professor and department head

Miguel A Martínez-González: Role: professor and department chair

Teresa T Fung: Role: professor

Alice H Lichtenstein: Role: Gershoff professor, director and senior scientist

Nita G Forouhi: Role: professor and programme leader

Journal

Journal ID (nlm-ta): BMJ

Journal ID (iso-abbrev): BMJ

Journal ID (publisher-id): BMJ-UK

Journal ID (hwp): bmj

Title: The BMJ

Publisher: BMJ Publishing Group Ltd.

ISSN (Print): 0959-8138

ISSN (Electronic): 1756-1833

Publication date Collection: 2018

Publication date (Electronic): 13 June 2018

Volume: 361

Electronic Location Identifier: k2396

Affiliations

[1 ]Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.

[2 ]University of Potsdam, Institute of Nutritional Sciences, Nuthetal, Germany

[3 ]NutriAct—Competence Cluster Nutrition Research Berlin-Potsdam, Germany

[4 ]Department of Preventive Medicine and Public Health, University of Navarra-IdiSNA, Pamplona, Spain

[5 ]CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Spain

[6 ]Department of Nutrition, Harvard T H Chan School of Public Health, Boston, USA

[7 ]Department of Nutrition, Simmons College, Boston, USA

[8 ]Friedman School of Nutrition Science and Policy, Tufts University, Boston, USA

[9 ]Cardiovascular Nutrition Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, USA

[10 ]Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK

Author notes

Correspondence to: M B Schulze mschulze@ 123456dife.de

Article

Publisher ID: schm045209

DOI: 10.1136/bmj.k2396

PMC ID: 5996879

PubMed ID: 29898951

SO-VID: d64d7741-8f6c-4873-a080-cfabf2c122f5

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This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

Food based dietary patterns and chronic disease prevention

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Tobacco Prevention & Cessation

Most cited references 28

Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis.

Application of a new statistical method to derive dietary patterns in nutritional epidemiology.

Comparison of weight loss among named diet programs in overweight and obese adults: a meta-analysis.

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