In the past, researchers looked at the relationship between individual foods and nutrients and health. Today, there is interest in looking at how combinations of foods and beverages, or dietary patterns, impact health. Statistical methods called factor and cluster analyses can be used to describe the patterns of foods and beverages people eat. This summary of a NEL review presents what we know about dietary patterns of certain groups of people described using factor and cluster analysis and the likelihood of becoming overweight or obese.

Conclusion

Limited and inconsistent evidence from epidemiological studies examining dietary patterns derived using factor or cluster analysis in adults found that consumption of a dietary pattern characterized by vegetables, fruits, whole grains, and reduced-fat dairy products tends to be associated with more favorable body weight status over time than consumption of a dietary pattern characterized by red meat, processed meats, sugar-sweetened foods and drinks, and refined grains.

What the Research Says

• Results from the 11 studies included in this review tell us that dietary patterns high in vegetables, fruits, low-fat dairy products, and whole grains may prevent adults from gaining weight.
• Consuming a diet pattern high in red meat, processed meats, sugar-sweetened foods and drinks, and refined grains may increase the likelihood of weight gain in adults.

Background

The goal of this systematic review project is to identify patterns of food and beverage intake that promote health and prevent disease. Historically, most dietary guidance has been based on research conducted on individual food components or nutrients. Dietary patterns are defined as the quantities, proportions, variety, or combination of different foods, drinks, and nutrients (when available) in diets, and the frequency with which they are habitually consumed. Factor and cluster analysis allow examination of the relationship between prevailing dietary patterns of a population and outcomes of public health concern. The objective of this systematic review was to assess the relationship between patterns of food and beverage intake identified using factor and cluster analysis, and risk of obesity.

Conclusion Statement 

Limited and inconsistent evidence from epidemiological studies examining dietary patterns derived using factor or cluster analysis in adults found that consumption of a dietary pattern characterized by vegetables, fruits, whole grains, and reduced-fat dairy products tends to be associated with more favorable body weight status over time than consumption of a dietary pattern dominated by red meat, processed meats, sugar-sweetened foods and drinks, and refined grains.

Methods 

Literature searches were conducted using PubMed, Embase, Navigator (BIOSIS, CAB Abstracts, and Food Science and Technology Abstracts) and Cochrane databases to identify studies that evaluated the association between dietary patterns defined using factor or cluster analysis and risk of obesity. Studies that met the following criteria were included in the review: Human subjects; Ages: 2 years and older; Populations: Healthy and those with elevated chronic disease risk; subjects from countries with high or very high human development (based on the 2011 Human Development Index); randomized controlled trials, non-randomized controlled trials, or quasi-experimental studies; Sample size: Minimum of 30 subjects per study arm; Dropout rate less than 20 percent; Study assesses dietary intake using factor and cluster analysis; study considered body weight and risks of overweight and obesity; published in English in a peer-reviewed journal. The date range for the conduct of studies was unlimited. 

The results of each included study were summarized in evidence worksheets (including a study quality rating) and evidence table. A group of subject matter experts were involved in a qualitative synthesis of the body of evidence, development of a conclusion statement, and assessment of the strength of the evidence (grade) using pre-established criteria including evaluation of the quality, quantity, consistency, magnitude of effect, and generalizability of available evidence. 

Findings

Eleven prospective cohort studies, ranging in length from 3 to 20 years, examined dietary patterns and their association with body weight. To derive dietary patterns, seven studies used factor analysis and four studies used cluster analysis. Eight studies were conducted in the United States, with three additional studies from the United Kingdom, Iran, and Denmark. Sample sizes ranged from 206 to 51,670 participants.

• Variability in the studies included in this review, including populations considered, dietary assessment methods used, the number and type of food groupings included in the analyses, and the statistical techniques employed, made comparisons among studies challenging.
• The number of patterns identified in the studies ranged from 2 to 6 and some similarities emerged among them. The patterns were not consistently defined by specific foods but rather by a range of foods with overlap among the patterns. What differentiated the patterns was the amount or frequency of each food consumed.
• Dietary patterns derived from factor or cluster analysis that were associated with lower risk of obesity were characterized by the presence of vegetables, fruit, whole grains, and reduced-fat dairy. In adults, results pointed toward a more favorable weight status, lower weight/waist circumference (WC) gain, and lower body mass index (BMI) over time.
• Dietary patterns that emerged in factor or cluster analysis associated with a higher risk of obesity were characterized by the presence of red meat and processed meats, sugar-sweetened foods and drinks, and refined grains. Results related to consumption of these patterns pointed toward increased body weight and waist circumference measures over time.

Discussion 

The ability to draw strong conclusions was limited due to the following issues: 

• In factor and cluster analysis, the consolidation of food items into food groups, the number of factors or clusters to extract, and even the labeling of components are subjective. Furthermore, patterns derived from either factor or cluster analysis may not be reproducible across studies because elements of dietary patterns and analytic decisions differ. 
• Dietary pattern analysis using factor or cluster methods may not be very informative in determining which elements of the diet or which biological relationships between these elements are responsible for the health outcome. 
• Some studies completed over long periods of time did not account for changes to subjects’ diets or seasonal variations in food supplies, which may have influenced the food components of patterns. 
• The patterns derived through analyses may not represent the most beneficial or detrimental patterns relative to the health outcome of interest.