healthy diet research topic

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Publications
• Account settings
• Advanced Search
• Journal List

The Importance of Healthy Dietary Patterns in Chronic Disease Prevention 1

Marian l neuhouser , phd, rd.

• Author information
• Article notes
• Copyright and License information

Correspondence to: Marian L Neuhouser, PhD, RD, Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave North, M4-B402, Seattle, WA 98109-1024, Tel: 206-667-4797, Fax: 206-667-7850, [email protected]

Issue date 2019 Oct.

The prevalence of chronic diseases in the United States and around the world is very high and not sustainable by most health care systems. While the etiology is complex, many chronic diseases are preventable through life long practices of adhering to healthy dietary patterns, engaging in physical activity and maintaining acceptable weight. Healthy dietary patterns were defined in the 2015 Dietary Guidelines Advisory Committee Scientific Report as diets that are high in fruits, vegetables, whole grains, low and non-fat dairy and lean protein. Other characteristics of healthy dietary patterns are that they are low in saturated fat, trans fat, sodium and added sugars. The preponderance of evidence to date suggests that healthy dietary patterns reduce the risk of the major diet-related chronic diseases, such as diabetes, cardiovascular disease and some cancers. While several methods exist for assessing dietary patterns in population studies, those that characterize dietary patterns using a priori scoring systems of indices, such as the Healthy Eating Index, may be of the most value because they offer a consistent metric that can be applied across multiple studies. It follows that consistency in methods then allows comparisons of results across populations. The nutrition science community can play a major leadership role in national and global health by promoting access to the ability of all population groups to consume a healthy dietary pattern.

Keywords: chronic diseases, dietary patterns, Healthy Eating Index

1. Introduction

Over the past century, the incidence, morbidity and mortality for non-communicable or chronic diseases has surpassed that for infectious or communicable diseases in the United States and much of the globe [ 1 ]. The ten leading causes of death in the United States are chronic diseases and their rank order is heart disease (1), cancer (2), cerebrovascular disease (5), and diabetes mellitus (7). In addition, over two thirds of US adults are overweight or obese [body mass index (BMI) > 30.0 kg/m 2 ] [ 2 ]. Obesity is both a chronic disease itself and it is an antecedent or risk factor for most of the aforementioned major causes of morbidity and mortality. Despite health campaigns to increase public awareness of obesity in recent years (e.g., Let’s Move https://letsmove.obamawhitehouse.archives.gov/ ) , the lack of evidence that the obesity epidemic is decreasing or even plateauing is quite concerning for the future health of the nation. These diet-related health risks are a problem around the globe as well [ 3 – 5 ].

2. Chronic disease incidence

About half of all Americans (~ 117 million individuals) have one or more preventable chronic disease and many millions more have chronic disease risk factors, such as hypertension or dyslipidemia [ 6 , 7 ]. This very high prevalence of chronic disease places tremendous stress on the health care system, decreases economic productivity due to disease-related disability and contributes to poor quality of life for millions of people and their families. The extent of the economic and social burden varies across population sub-groups such as racial/ethnic groups, age, geographic locale and socio-economic status [ 6 – 9 ]. For example, while the prevalence of overweight and obesity are unacceptably high among all Americans, the age-adjusted prevalence is higher among adult Hispanics (78.8%) and Black/African-Americans (76.7%) compared to non-Hispanic whites (68.0%) [ 10 ]. Similarly, diabetes mellitus is also more common in Hispanics (18.0%) and Black/African-Americans (18.0%) compared to non-Hispanic-whites (9.6%) [ 9 ]. Understanding risk factors, population distributions of disease prevalence, and the underlying etiology of chronic disease is one of the first steps to identifying effective programs for prevention, including programs for population subgroups who may be disproportionately represented in incidence and mortality estimates.

Chronic disease etiology is complex and multifactorial. Risk factors include age, family history, genetic predisposition, current and lifetime weight, current and lifetime physical activity, smoking, alcohol, and diet. Of these risk factors, the biggest public health impact will be made with reducing modifiable risk factors, such as diet. The nutrition science community can and should play a major leadership role in reducing the high and unsustainable level of preventable diet-related chronic diseases. In fact, some might suggest that nutrition scientists have a professional and moral obligation to do so. In broad terms, the task before us is to conduct high quality human nutrition research on diet and chronic disease risk to expand the evidence base from which policy decision will be made. Evidence-based clinical practice and policy should be applied to individuals, families, communities, clinicians and our society as a whole with the ultimate goal of improved population health.

3. Reducing diet-related chronic disease and dietary patterns

The first step in the endeavor to reduce diet-related chronic disease risk is to identify a framework for nutrition research. A useful dietary assessment approach that lends itself well to understanding the relationship of diet to chronic disease risk at the population level is dietary patterns [ 11 ]. Dietary patterns were defined by the 2015 Dietary Guidelines Advisory Committee as: “the quantities, proportions, variety or combination of different foods, drinks, and nutrients (when available) in diets and the frequency with which they are consumed” [ 12 ]. Dietary patterns considers the whole diet consumed by individuals and populations day-in and day-out over a period of months and years, as opposed to a reductionist approach that may focus on individual nutrients recorded as consumed on a single day or a few days. This distinction is important because the relationship between diet and chronic disease risk is a long-term exposure, as opposed to an acute exposure on a single day or even over the course of many short term exposures. Moreover, since diet-related chronic diseases have replaced nutrient deficiency diseases as the critical public health nutrition problems, this long-term total diet approach is one that is most suitable for chronic disease research.

Most of the research evidence base examining dietary patterns and chronic disease risk utilizes human prospective cohort studies where usual diet is assessed at baseline (i.e., cohort entry), and for some studies, at various follow-up time points over the course of many years [ 13 – 14 ]. This typically allows reasonable time for the diet (i.e., risk factor) to have a sufficient follow-up period to establish a meaningful and direct biological link to the disease outcome of interest (i.e., diabetes, cardiovascular disease), while also eliminating some of the bias that occurs with case-control and cross-sectional studies where temporality and directionality are not able to be established. Prospective cohorts are further well-suited to investigating diet-chronic disease relationships because the cohort design requires that participants are free of the disease endpoint at the time of enrollment. Disease endpoints or outcomes are accrued over the follow-up time period so the temporal sequence of diet-disease is more clearly and directly established. Additionally, prospective cohort studies collect detailed data on the primary exposures (in this case, diet) as well as variables that could be potential confounders of the relationship between diet and chronic disease outcomes, rendering more reliable and valid analytic models. Some of these confounders are physical activity, smoking and socioeconomic status. Failure to properly measure these confounders and include them in analysis could lead to spurious associations or null findings.

Several approaches have been used to characterize dietary patterns. Most prospective cohorts assess diet with one of many standardized self-reported dietary assessment approaches, such as food frequency questionnaires, 24-hour dietary recalls, or multiple day food records, or in some cases with objective nutritional biomarkers assessed from baseline blood or urine specimens. Dietary patterns are constructed from the self-reported data using 1) indices or a priori scoring systems; 2) patterns reported by the participants themselves; and/or 3) data driven approaches. For indices or scoring systems, investigators typically take the raw data, (i.e. foods and beverages reported as consumed) and apply one of several scoring systems to award points for consumption of food groups thought to be healthful (i.e., fruits, vegetables, whole grains), but no points or reverse scoring for less healthful foods or ingredients (i.e., added sugar, refined grains, red and processed meat). The particular foods or food groups included in these scoring systems or indices are based on empirical evidence for diet disease associations or based on other factors, such as government food policy recommendations. For example, the Healthy Eating Index (HEI) is based on adherence to the U.S Dietary Guidelines for Americans and the Dietary Approaches to Stop Hypertension (DASH) Diet Score is based on adherence to a diet that is similar to that prescribed in the DASH trial [ 15 – 16 ]. Each individual in the cohort is given a score that represents their usual diet or dietary pattern. The cohort members’ dietary pattern scores are then regressed on disease outcomes of interest, such as obesity, cardiovascular disease and cancer. The second approach also uses the dietary self-report of the cohort participants, but instead of a scoring system, patterns are characterized by a description from the participants themselves, such as self-reporting as vegetarian or lacto-ovo vegetarian. The third approach also uses the self-report data, but instead of investigator-driven scoring systems, analytic approaches such as principal components analysis and reduced rank regression are used to derive the patterns. While all three approaches have been used in prospective cohort studies, the broadest applications may be possible for the scoring system type approaches [ 11 , 13 , 14 ]. This is because the scoring systems or indices offer a consistent metric across multiple studies such that data can be compared across cohorts to evaluate consistency of associations. In addition, consistent metrics facilitate pooling of data to examine the totality of associations across the broader population. Further, scoring systems such as the HEI assess adherence to national dietary recommendations (the US Dietary Guidelines for Americans). In this manner, the link between research and policy is direct. In contrast, when a data driven approach is used to specify dietary patterns, tremendous heterogeneity emerges across cohorts since the loading factors often vary tremendously with little sense of consistency between populations studied [ 11 ]. One weakness of all of these approaches is that self-reported diet is used to assess the dietary patterns . Evidence has accumulated over the past 10 or so years that self-report is limited by systematic measurement error [ 17 ]. New and emerging approaches using nutritional biomarkers may have promise for characterizing dietary patterns [ 18 ]. Several studies have shown that the use of biomarkers, such as doubly labeled water for total energy intake and urinary nitrogen for protein intake are more reliable measures of these dietary components because they are objective and less subject to systematic error compared to self-report [ 17 , 18 ]. Research is very active in this area – particularly discovery research that aims to identify groups of biomarkers that could be used to characterize a pattern. For example, the use of metabolomics may uncover groups of metabolites that can characterize groups of foods or a pattern that may be predict chronic disease risk [ 19 ].

4. Communicating healthy dietary patterns

The concept of dietary patterns lends itself well to dissemination and implementation to the public. Diet is complex, consisting of hundreds of components and mixtures of foods spanning over 140 individual nutrients or nutritive compounds. Since the public eats food, not nutrients, recommending foods and food groups that promote health may be more easily adopted than keeping track of a myriad of nutrients. For this reason, the 2015 Scientific Report of the United States Dietary Guidelines Advisory Committee and the subsequent policy, the 2015–2020 Dietary Guidelines for Americans, advocated for use of healthy dietary patterns [ 12 , 20 ]. The Report describes a healthy dietary pattern as one that includes a variety of vegetables from all the subgroups (dark green, red, orange, legumes, starchy and others), fruits (especially whole fruits), grains- half of which should be whole grains, fat-free or low-fat dairy, a variety of protein foods – including seafood, lean meat, poultry, eggs, soy and oils. The Report further specified that a healthy dietary pattern limits saturated fat, trans fat, added sugars and sodium. These recommendations were based on systematic review of peer-reviewed literature. The Dietary Guidelines policy document recommending these healthy dietary patterns is used across US Departments and Agencies for a variety of food, health, consumer and agricultural programs to improve the nutritional status and health of the nation. Scientists, clinicians and policy makers recognize that thorough uptake of healthy dietary patterns across the entire population, including subgroups with multiple risk factors or higher prevalence of diet-related chronic diseases, will require concerted effort on the part of individuals, families, communities, industry and government.

5. Conclusions

This commentary has focused on chronic diseases that are common the United States and how the adoption of healthy dietary has potential for long lasting and sustained improved population health. Importantly, these principles can be applied more broadly to global health. The Global Burden of Disease Collaboration and others [ 3 – 5 ] have documented the rise in lifestyle related diseases around the globe; clearly, this is not a problem isolated to the West. For example, dietary guidelines committees and policies in Mexico have adopted approaches similar to those in the United States [ 21 , 22 ]

A shift towards healthy dietary patterns has the potential to curtail the current unsustainable high level of obesity, cardiovascular disease, diabetes mellitus and cancer in the United States and around the globe. The health and well-being of current and future generations is dependent upon good nutrition as a strong foundation for health.

Acknowledgment

Supported by R01 CA119171, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services

Abbreviations

Dietary Approaches to Stop Hypertension

Healthy Eating Index

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

This article was part of the presentations for the 2017 Korean Nutrition Society 50 th Anniversary International Conference, November 2–3, 2017 in Seoul Korea.

• [1]. Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff C, Kutz MJ, Huynh C et al. US county-level trends in mortality rates for major causes of death, 1980–2014. JAMA 2016; 316:2385–2401. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [2]. Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Odgen CL. Trends in obesity among adults in the United States 2005–2014. JAAM 2016; 315:2284–2291. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [3]. Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G et al. Global, regional and national burden of cardiovascular diseases for 10 causes, 1990 t0 2015. J Am Coll Cardiol 2017; 70:1015. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [4]. GBD 2015 Obesity Collaborators, Afshin A, Forozanfar MH, Reitsna MB, Sur P, Estep K et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 2017; 377: 13–27 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [5]. Lopez-Olmedo N, Popkin BM, Tallie LS. The socioeconomic disparities in intakes and purchases of less-healthy foods and beverages have changed over time in urban Mexico. J Nutr 2018; 148:109–116. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [6]. Murkamal KJ, Siscovick DS, de Boer IH, Ix JH, Kizer JR, Djousse L et al. Metabolic clusters and outcomes in older adults: the cardiovascular health study. Journal of the American Geriatrics Society 12 February 2018. [Epub before print]. DOIU 10.1111/jgs.15205. [ DOI ] [ PMC free article ] [ PubMed ]
• [7]. 40 th Annual report on the health of the nation features long-term trends in health and health care delivery in the United States. Centers for Disease Control and Prevention, US Department of Health and Human Services, National Center for Health Statistics, DHHS Publication No. 2017–1232, May 2017. [ Google Scholar ]
• [8]. Mokdad AH, Dwyer-Lindgren L, Fitzmaurice C, Stubbs RW, Bertozzi-Villa A, Morozoff C et al. Trends and patterns of disparities in cancer mortality among US counties, 1980–2014. JAMA 2017; 317:388–406. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [9]. Roth GA, Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff C. Naghavi M et al. Trends and patterns of geographic variation in cardiovascular mortality among US counties 1980–2014. JAMA 2017; 317:1976–1992. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [10]. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAAM 2012; 307:491–497. [ DOI ] [ PubMed ] [ Google Scholar ]
• [11]. Liese AD, Krebs-Smith SM, Subar AF, George SM, Harmon BE, Neuhouser ML et al. The Dietary Patterns Methods Project: Synthesis of Findings across Cohorts and Relevance to Dietary Guidance. J Nutr 2015; 145(3):393–402. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [12]. Millen BE, Abrams S, Adams-Campbell L, Anderson CA, Brenna JT, Campbell WW et al. The 2015 Dietary Guidelines Advisory Committee Scientific Report: Development and Major Conclusions. Adv Nutr May 2016; 7(3):438–44. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [13]. George SM, Ballard-Barbash R, Manson JE, Reedy J, Shikany JM, Subar AF et al. Comparing Indices of Diet Quality With Chronic Disease Mortality Risk in Postmenopausal Women in the Women’s Health Initiative Observational Study: Evidence to Inform National Dietary Guidance. Am J Epidemiol 2014; 180(6):616–25. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [14]. Cespedes EM, Hu FB, Tinker L, Rosner B, Redline S, Garcia L et al. Multiple Healthful Dietary Patterns and Type 2 Diabetes in the Women’s Health Initiative. Am J Epidemiol 2016. April 1; 183(7):622–33. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [15]. Appel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM et al. A clinical trial of the effects of dietary patterns on blood pressure. DASH Collaborative Research Group. N Engl J Med 1997; 336: 1117–24. [ DOI ] [ PubMed ] [ Google Scholar ]
• [16]. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-sodium Collaborative Research Group. N Engl J Med 2001; 344:3–10. [ DOI ] [ PubMed ] [ Google Scholar ]
• [17]. Neuhouser ML, Tinker L, Shaw PA, Schoeller D, Bingham SA, van Horn LV, et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women’s Health Initiative. Am J Epidemiol 2008; 167(10): 1247–59. [ DOI ] [ PubMed ] [ Google Scholar ]
• [18]. Prentice RL, Tinker LF, Huang Y, Neuhouser ML. Calibration of self-reported dietary measures using biomarkers: an approach to enhancing nutritional epidemiology reliability. Curr Atheroscler Rep 2013; 15(9):353. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [19]. Playdon MC, Ziegler RG, Sampson JN, Stolzenberg-Solomon R, Thompson HJ, Irwin ML et al. Nutritional Metabolomics and breast cancer risk in a prospective study. Am J Clin Nutr 2017; 106:637–49. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [20]. US Department of Health and Human Services and US Department of Agriculture. 2015–2010 Dietary Guidelines for Americans 8th Edition. December 2015
• [21]. Perez-Escamilla R The Mexican dietary and physical activity guidelines: moving public nutrition forward in a globalized world. J Nutr 2016; 146: 1924S–7S [ DOI ] [ PubMed ] [ Google Scholar ]
• [22]. Popkin BM, Hawkes C. Sweetening of the global diet, particularly beverages: patterns, trends and policy responses. Lancet Diabetes Endocrinol 2016; 4:174–86. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• View on publisher site
• PDF (292.4 KB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

• Download .nbib .nbib
• Format: AMA APA MLA NLM

Add to Collections