Ultra-processed food consumption and cardiometabolic risk in Canada: a cross-sectional analysis of the Canadian health measures survey
Bibliographic record
Abstract
BACKGROUND: Ultra-processed food (UPF) contributes to nearly 50% of Canadians' diets. Research in other countries has begun to implicate high intakes of UPFs and negative health outcomes, including body mass index, waist circumference, blood pressure, and unfavourable lipid profiles. There have been no population level examinations of the relationship between UPF consumption and cardiometabolic risk in Canada. METHODS: Drawing on the Canadian Health Measures Survey (2016/17 and 2018/19), this study investigates the relationship between UPF consumption and cardiometabolic risk factors among Canadians (ages 19-79, n = 6517). Dietary data collected by Food Frequency Questionnaire was classified as UPF or not using the NOVA classification system which scores foods by degree of processing. Participants were grouped into quartiles based on the daily servings of UPF. Sociodemographic and lifestyle variables were collected via household questionnaire and cardiometabolic outcomes were measured during a clinic visit. Multivariable linear regression analyses separately assessed the association between cardiometabolic risk factors and UPF quartiles while adjusting for various sociodemographic and lifestyle variables. Sensitivity analyses additionally adjusted for fruit and vegetable intake (servings/day) to determine the effect of diet quality on this relationship. All analyses were weighted to ensure national representativeness. RESULTS: UPF servings per day ranged from 1.2 in the lowest and 5.8 in the highest quartile. Compared to the lowest quartiles of UPF consumption, those in the highest were more likely to be male, in the lowest income quartile, Black or White, have lower household education, and higher physical activity and sedentary time. After adjustments, UPF consumption was positively associated with BMI, WC, diastolic BP, HBA1C, c-reactive protein, white blood cells (WBC), fasting triglycerides (TG), and fasting insulin. Fruit and vegetable intake attenuated the association for all outcomes, while BMI, WC, WBC, and TG remained significantly associated with increased UPF consumption. CONCLUSION: This study is the first Canadian study looking at population level intakes of UPF across various cardiometabolic risk factors and adds to the growing body of literature demonstrating the detrimental health effects associated with UPF consumption.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".