Fast-Food Dietary Pattern Is Linked to Higher Prevalence of Metabolic Syndrome in Older Canadian Adults
Bibliographic record
Abstract
Background. Metabolic syndrome (MetS) is known to increase the risk of cardiovascular diseases and diabetes. Diet is a key factor in prevention and development of MetS. This study aimed to determine the association between dietary patterns and MetS among Canadians 12–79 years old using the Canadian Health Measures Survey (CHMS) combined Cycles 1 and 2 data from 2007–11. We hypothesized that MetS varies among different sociodemographic and lifestyle factors and that Canadians who have less healthy dietary patterns are more likely to have MetS. Methods. In the CHMS, MetS was determined using objective health measures. The principal component analysis method was used to determine the dietary patterns. Using logistic regression, the association between MetS and dietary patterns, controlling for potential covariates, was investigated for age groups of 12–19, 20–49, and 50–79 years. Survey data were weighted and bootstrapped to be representative at the national level. Results. The prevalence of MetS was 16.9% for ages 12–79 y (n = 4,272, males = 49.6%), representing 26,038,108 Canadians aged 12–79 years. MetS was significantly different across sociodemographic variables; Canadians with less education, income, and activity had higher MetS prevalence than their counterparts. In older adults (50–79 years of age), the “fast-food” dietary pattern was associated with 26% (odds ratio = 1.26; 95% CI: 1.04 to 1.54; <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> <a:mo>=</a:mo> <a:mn>0.0195</a:mn> </a:math> ) higher likelihood of having MetS. Conclusions. Among older Canadians, MetS is associated with a “fast-food” dietary pattern after adjustment for socioeconomic/lifestyle factors. Findings suggest the importance of diet quality/composition in the development of MetS among older Canadians and the need for further longitudinal studies on MetS and diet across the lifespan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".