Identification of an Obesogenic Dietary Pattern Using Partial Least Squares in a Nationally-Representative Sample of Canadian Adults
Bibliographic record
Abstract
Hybrid methods of dietary patterns analysis have emerged as a unique and informative way to study diet-disease relationships in nutritional epidemiology research. The objectives of this research were to identify an obesogenic dietary pattern using weighted PLS in nationally-representative Canadian survey data, and to identify key foods and/or beverages associated with the defined obesogenic pattern. Data from one 24-hr dietary recall data from the cross-sectional Canadian Community Health Survey-Nutrition (CCHS) 2015 (n = 12,110 adults) were used. Weighed partial least squares (wPLS) was used to identify an obesogenic dietary pattern from 40 standardized food and/or beverage categories using the variables energy density, fibre density, and total fat as outcomes. The association between the derived dietary pattern and likelihood of obesity was examined using weighted multivariate logistic regression. Key dietary components highly associated with the derived pattern were identified. Compared to quartile one (i.e., those least adherent to an obesogenic dietary pattern), those in quartile four had a 2.40-fold increased odds of being obese (OR = 2.40, 95% CI = 1.91, 3.02, P-trend < 0.0001) with a monotonically increasing trend. Using a factor loading significance cut-off of ≥|0.17|, three food/beverage categories loaded positively for the derived obesogenic dietary pattern: fast food, carbonated drinks and salty snacks. Seven food/beverage categories loaded negatively (i.e., in the protective direction): consumption of whole fruits, orange vegetables, “other” vegetables (including vegetable juice), whole grains, dark green vegetables, legumes and soy, and pasta and rice. This study pinpoints key dietary components that are associated with obesity and consumed among a nationally-representative sample of Canadians adults. Compared to a similarly-defined obesogenic diet identified by our research group in 2004, the top contributors to a Canadian-specific obesogenic diet in 2015 have remained consistent. This evidence may aid in developing targeted policies and dietary interventions for obesity and chronic disease prevention. Supported by grants from the Burroughs Wellcome Fund Innovation in Regulatory Science Award and the Canadian Institutes of Health Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".