Identification of Dietary Patterns Associated with Chronic Disease Risk Using Hybrid Dimension Reduction Techniques: Evidence from the Canadian National Nutrition Survey

Abstract

Analysing dietary patterns is an important approach for characterizing the complex relationships between foods and nutrients in etiology of obesity and other chronic diseases. Several studies have used a priori and data‐driven dimension reduction techniques for evaluating dietary patterns in relation to chronic disease risks, even though methods for applying these techniques to complex nationally‐representative nutrition surveys are not yet developed. The objective of this study was to define a novel algorithm for using hybrid dimension reduction techniques for identifying dietary patterns of Canadians most strongly associated with obesity and other chronic diseases (diabetes, cancer, and cardiovascular diseases) at the national population level. Dietary data were collected using 24‐hour dietary recalls (second recall in sub‐sample). All analyses included 11,748 participants (≥18 y) in the cross‐sectional nationally‐representative Canadian Community Health Survey 2.2 (2004/5). To ensure nationally‐representative estimates, weighting algorithm was incorporated into the partial least squares analyses (PLS), to derive an energy‐dense (ED), high‐fat (HF) and low fiber density (LFD) dietary pattern using 38 food groups. PLS is the most flexible hybrid technique for deriving dietary patterns enabling discovery of important disease‐specific dietary exposures that have not been previously identified in etiology of chronic diseases. The association of dietary patterns with obesity and chronic diseases was ascertained using weighted multinominal logistic regression‐GLM adjusted for the following covariates in successive models: age, sex, dietary misreporting, energy intakes, physical activity and smoking. Using the weighted PLS algorithm, an ED,HF,LFD dietary pattern was derived with high positive loadings for fast foods, carbonated drinks, refined grains and negative loadings for whole fruits, and vegetables (≥|0.17|). Food groups with a “high” loading were summed to form a simplified dietary pattern score. Moving from the first (healthiest) to the fourth (least healthy) quartiles of the ED,HF,LFD and the simplified dietary pattern scores was associated with increasingly elevated odds ratios (OR) for “obesity with at least one chronic disease” (diabetes, cancer and cardiovascular diseases), with individuals in quartile 4 having an OR of 2.57 (95%CI:1.75,3.76) and 2.73 (1.88,3.98), respectively (p‐trend<0.0001). The associations of dietary patterns with “healthy obesity” (obesity without having a chronic disease) and “being non‐obese with at least one chronic disease” were weaker, albeit significant (p<0.05). Overall, consuming an ED,HF,LFD dietary pattern was associated with significantly higher risk of obesity with and without accompanying chronic diseases. Our findings demonstrated that novel techniques for deriving dietary patterns can be modified for successful use in nationally‐representative surveys. The weighted algorithm we defined in this research can be used for deriving dietary patterns associated with chronic diseases at the national population level, improving the applicability and use of novel dietary pattern techniques by governments and researchers.Support or Funding InformationThis research was supported by a grant from the Burroughs Wellcome Fund Innovation in Regulatory Science Award, and funds to the Canadian Research Data Centre Network (CRDCN) from the Social Science and Humanities research Council (SSHRC), the Canadian Institutes of Health Research (CIHR), the Canadian Foundation for Innovation (CFI) and Statistics Canada. M.J. was funded by a Burroughs Wellcome Fund Fellowship, the Canadian Institutes of Health Research (CIHR) Vanier Canada Graduate Scholarship, the CIHR/Cancer Care Ontario (CCO) Population Intervention for Chronic Disease Prevention (PICDP): A Pan‐Canadian Fellowship, Ontario Graduate Scholarship (OGS) and the Faculty of Medicine Hunter Fellowship (University of Toronto). M.L. is the Earle W. McHenry professor and is supported through chair endowed unrestricted research funds, University of Toronto. Funders had no role in the design, analysis or writing of this article.